The IEEE International School of Imaging (I2SI) will take place October 14-16, 2014 on the island of Santorini, Greece. The school will explore principles and advancements in imaging technologies for medical diagnostics, pharmaco-imaging, remote sensing, and more. Engineers, scientists, and medical professionals are invited to attend lectures from worldwide experts and interact with others working to advance imaging sciences. Topics will include medical imaging modalities, nanoscale oncology, space instrumentation, semiconductor inspection, and more. The goal is to foster development of novel imaging technologies and applications across various disciplines.
Slides presented at the Molecular Med Tri-Con 2018 Precision Medicine, "Emerging Role of Radiomics in Precision Medicine" (http://www.triconference.com/Precision-Medicine/)
Abstract
The goal of this talk is to discuss the role of data standards, and specifically the Digital Imaging and Communication in Medicine (DICOM) standard, in supporting radiomics research. From the clinical images, to the storage of image annotations and results of radiomics analysis, standardization can potentially have transformative effect by enabling discovery, reuse and mining of the data, and integration of the radiomics workflows into the healthcare enterprise.
This document provides an overview of the Department of Biomedical Engineering at Eindhoven University of Technology. It discusses general trends in healthcare technology and computational biology examples. The department focuses on areas like regenerative medicine, chemical biology, computational diagnostics, and biomechanics & tissue engineering. Research groups within the department work on topics such as cardiovascular biomechanics, cell-matrix interaction, molecular biosensing, and medical image analysis. The document also provides information on the department's educational programs, collaborations, budgets, and key personnel.
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the
surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death
and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming
rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in
medical imaging. Automation of tumor detection is required because there might be a shortage of skilled
radiologists at a time of great need. This paper reviews the processes and techniques used in detecting tumor
based on medical imaging results such as mammograms, x-ray computed tomography (x-ray CT) and
magnetic resonance imaging (MRI). We find that computer vision based techniques can identify tumors
almost at an expert level in various types of medical imagery assisting in diagnosing myriad diseases.
Medical image processing involves acquiring medical images through modalities like X-rays, CT, MRI, using techniques like ultrasound. The images are then preprocessed, segmented, analyzed and classified to diagnose diseases or detect abnormalities. Key applications include tumor detection, monitoring bone strength, and medical image fusion to enable accurate analysis and remote sharing of data to enhance diagnosis and treatment.
This document provides an overview of medical devices, including definitions, classifications, examples of different types of devices used for diagnosis, treatment and monitoring, and the theoretical principles underlying their functions. It discusses the purpose of medical biophysics education which is to ensure safe and effective use of devices through understanding their physics and applying protocols to minimize risks to patients and users. Competencies for medical device users are outlined, such as understanding device functions, limitations, quality control, and adhering to relevant regulations.
Model guided therapy and the role of dicom in surgeryKlaus19
1. Model-guided therapy uses patient-specific models to complement image-guided therapy, bringing treatment closer to precise diagnosis, accurate prognosis assessment, and individualized planning and validation of therapy.
2. TIMMS is an IT system that facilitates model-guided therapy through interoperability of data, images, models, and tools to support the therapeutic intervention.
3. Patient-specific models in TIMMS must represent multidimensional and multiscale patient data, interface various system components, and link model components meaningfully while maintaining model accuracy over time.
Machine Learning for Medical Image Analysis:What, where and how?Debdoot Sheet
A great career advice for EECS (Electrical, electronics and computer science) graduates interested in machine vision and some advice for a PhD career in Medical Image Analysis.
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the computational intelligence.
Slides presented at the Molecular Med Tri-Con 2018 Precision Medicine, "Emerging Role of Radiomics in Precision Medicine" (http://www.triconference.com/Precision-Medicine/)
Abstract
The goal of this talk is to discuss the role of data standards, and specifically the Digital Imaging and Communication in Medicine (DICOM) standard, in supporting radiomics research. From the clinical images, to the storage of image annotations and results of radiomics analysis, standardization can potentially have transformative effect by enabling discovery, reuse and mining of the data, and integration of the radiomics workflows into the healthcare enterprise.
This document provides an overview of the Department of Biomedical Engineering at Eindhoven University of Technology. It discusses general trends in healthcare technology and computational biology examples. The department focuses on areas like regenerative medicine, chemical biology, computational diagnostics, and biomechanics & tissue engineering. Research groups within the department work on topics such as cardiovascular biomechanics, cell-matrix interaction, molecular biosensing, and medical image analysis. The document also provides information on the department's educational programs, collaborations, budgets, and key personnel.
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the
surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death
and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming
rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in
medical imaging. Automation of tumor detection is required because there might be a shortage of skilled
radiologists at a time of great need. This paper reviews the processes and techniques used in detecting tumor
based on medical imaging results such as mammograms, x-ray computed tomography (x-ray CT) and
magnetic resonance imaging (MRI). We find that computer vision based techniques can identify tumors
almost at an expert level in various types of medical imagery assisting in diagnosing myriad diseases.
Medical image processing involves acquiring medical images through modalities like X-rays, CT, MRI, using techniques like ultrasound. The images are then preprocessed, segmented, analyzed and classified to diagnose diseases or detect abnormalities. Key applications include tumor detection, monitoring bone strength, and medical image fusion to enable accurate analysis and remote sharing of data to enhance diagnosis and treatment.
This document provides an overview of medical devices, including definitions, classifications, examples of different types of devices used for diagnosis, treatment and monitoring, and the theoretical principles underlying their functions. It discusses the purpose of medical biophysics education which is to ensure safe and effective use of devices through understanding their physics and applying protocols to minimize risks to patients and users. Competencies for medical device users are outlined, such as understanding device functions, limitations, quality control, and adhering to relevant regulations.
Model guided therapy and the role of dicom in surgeryKlaus19
1. Model-guided therapy uses patient-specific models to complement image-guided therapy, bringing treatment closer to precise diagnosis, accurate prognosis assessment, and individualized planning and validation of therapy.
2. TIMMS is an IT system that facilitates model-guided therapy through interoperability of data, images, models, and tools to support the therapeutic intervention.
3. Patient-specific models in TIMMS must represent multidimensional and multiscale patient data, interface various system components, and link model components meaningfully while maintaining model accuracy over time.
Machine Learning for Medical Image Analysis:What, where and how?Debdoot Sheet
A great career advice for EECS (Electrical, electronics and computer science) graduates interested in machine vision and some advice for a PhD career in Medical Image Analysis.
Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the computational intelligence.
University of Toronto - Radiomics for Oncology - 2017Andre Dekker
This document contains the slides from a lecture on radiomics for oncology given by Andre Dekker. The lecture covers the rationale for radiomics, which is to use quantitative features extracted from medical images to help predict outcomes like tumor behavior, survival, and response to treatment using machine learning. The major workflow steps of radiomics are discussed, from image acquisition and feature extraction to modeling and validation. Key challenges like robust segmentation and feature reproducibility are also addressed. New directions for radiomics research include applications in preclinical studies, other modalities like PET and MRI, and linking radiomic features to genomic data. Overall, radiomics holds promise to help personalized medicine but large amounts of standardized data are still needed for proper validation of models.
Public Databases for Radiomics Research: Current Status and Future DirectionsCancerImagingInforma
This document discusses radiomics research and public databases. It describes what radiomics is and why data sharing is important. Several public databases are mentioned, with an in-depth look at The Cancer Imaging Archive (TCIA). TCIA hosts radiology data like CT, MR, PET images along with associated data. It provides services to upload and access data and enables data citation. Future directions discussed include standardization initiatives and using cloud computing.
The document discusses deep learning applications for medical image analysis, including for diagnosis, surgical planning and guidance, and risk assessment. Specifically, it presents examples of using deep learning for tasks like classification, segmentation, detection, and pose estimation using medical images from modalities like ultrasound, X-ray, and video. Challenges in the field include limited datasets, variability in medical images, and privacy concerns, but deep learning methods are able to learn features directly from data to help with complex medical image analysis problems.
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
Felipe Campos Kitamura is a medical doctor, radiologist, and AI practitioner whose research interests include medical imaging, computer vision, artificial intelligence, and machine learning. He is currently focused on using machine learning in healthcare applications such as medical imaging analysis and using AI to help summarize surgical events in real-time. Machine learning can be applied in healthcare for tasks like medical diagnosis, predictive analytics for disease screening and monitoring, and assisting with surgical procedures.
Imaging biobanks, report from the european society of radiology Emanuele Neri
In March 2014 the European Society of Radiology (ESR) established a dedicated working group (ESR WG on Imaging Biobanks) aimed at monitoring the existing imaging biobanks in Europe, promoting the federation of imaging biobanks and
communication of their findings in a white paper. The WG provided the following statements:
Imaging biobanks can be defined as “organised databases of medical images and associated imaging biomarkers (radiology and beyond) shared among multiple researchers, and linked to other biorepositories”.
The immediate purpose of imaging biobanks should be to allow the generation of imaging biomarkers for use in research studies and to support biological validation of existing and novel imaging biomarkers.
A long-term scope of imaging biobanks should be the creation of a network/federation of such repositories integrated with the already existing biobanking network.
DiaMe: IoMT deep predictive model based on threshold aware region growing tec...IJECEIAES
Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.
Comparative Study on Cancer Images using Watershed Transformationijtsrd
Digital images are exceptionally huge in the medical image diagnosis frameworks. Image analysis and segmentation are very important tasks in the medical image processing particularly in the field of CAD systems. Visual inspection requires being clear in diagnosis process where the correct region which is affected, need to be separated. Medical imaging plays a very crucial role in all stages of the medical decision process. There are various medical imaging modalities in which mammography are used to detect breast cancer where as MRI for brain tumor and CT for lung cancer. The objective of this paper is to compare the cancer images with different modalities using watershed transformation using metrics. M. Najela Fathin | Dr. S. Shajun Nisha | Dr. M. Mohamed Sathik"Comparative Study on Cancer Images using Watershed Transformation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12767.pdf http://www.ijtsrd.com/computer-science/other/12767/comparative-study-on-cancer-images-using-watershed-transformation/m-najela-fathin
Pathomics Based Biomarkers and Precision MedicineJoel Saltz
Role of Digital Pathology Data Science (Pathomics) in precision medicine. Features from billions or trillions of objects segmented from digital Pathology data can be employed to predict patient outcome and steer treatment.
Presentation at Imaging 2020, Jackson Hole, WY September 2016
3D printing offers benefits for healthcare such as rapid prototyping, mass customization, and ability to create complex geometries that cannot be made through other methods. However, it also faces limitations including being time consuming for all but rare cases, limited material selection, size limitations, and lack of creative uses. Areas that have seen success with 3D printing include prosthetics/exoskeletons through improved fitting, design features, aesthetics and cost reduction. It also shows promise for pre-surgical applications like planning, education, training and surgical guides as well as fields like bioprinting, tissue engineering, and medical education. For 3D printing to reach its potential in healthcare, reimbursement, regulatory approval,
Advances in automatic tuberculosis detection in chest x ray imagessipij
Tuberculosis (TB) is very dangerous and rapidly spread disease in the world. In the investigating cases for
suspected tuberculosis (TB), chest radiography is not only the key techniques of diagnosis based on the
medical imaging but also the diagnostic radiology. So, Computer aided diagnosis (CAD) has been popular
and many researchers are interested in this research areas and different approaches have been proposed
for the TB detection and lung decease classification. In this paper, the medical background history of TB
decease in chest X-rays and a survey of the various approaches in TB detection and classification are
presented. The literature in the related methods is surveyed papers in this research area until now 2014.
On March 23, 2016, Prof. Henning Müller (HES-SO Valais-Wallis and Martinos Center) presented Medical image analysis and big data evaluation infrastructures at Stanford medicine.
Pathomics Based Biomarkers, Tools, and Methodsimgcommcall
This document discusses pathomics-based biomarkers, tools, and methods for multi-scale integrative analysis in biomedical informatics. It summarizes several projects involving extracting quantitative features from pathology and radiology images using image segmentation and analysis techniques. These features are then linked to molecular data and clinical outcomes using statistical and machine learning methods to develop biomarkers. The tools and methods described aim to standardize and optimize feature extraction while accounting for uncertainties.
This document summarizes a seminar on knowledge engineering in oncology given by Andre Dekker. The seminar discusses how big data and machine learning can help address challenges in predicting cancer outcomes and personalizing treatment. It notes that doctors cannot reliably predict outcomes and that data sharing is difficult. However, distributed models that analyze data where it resides without sharing it can help rapidly learn from large numbers of patients. The seminar describes examples of collaborative data networks and predictive models developed for applications like lung cancer stereotactic body radiotherapy and proton therapy patient selection. The goal is developing tools like predictive apps to empower citizens in making healthcare decisions.
ARTIFICIAL NEURAL NETWORKING.
FIRST STEP TO KNOWLEDGE IS TO KNOW THAT we are ignorant
Knowledge in medical field is characterized by uncertanity and vagueness
Historically as well as currently this fact remains a motivation for the development of medical decision support system are based on fuzzy logics
Greek philosopher visualized a basic model of brain function as early as 300 bc
Till date nervous system is not completely understood to human kind.
Dekker trog - radiomics for oncology - 2017Andre Dekker
This document discusses radiomics, which is the process of extracting large amounts of quantitative features from medical images to help diagnose and predict treatment outcomes for cancer patients. It describes the typical radiomics workflow, which involves image segmentation, feature extraction, and building predictive models. It also discusses some challenges in radiomics like accounting for differences in imaging protocols and robust segmentation. The document outlines new directions for radiomics, including applications to preclinical studies, PET imaging, MRI, change over time ("delta radiomics"), combining with genomics ("radiogenomics"), and distributed learning approaches.
Dekker trog - big data for radiation oncology - 2017Andre Dekker
- Big data in radiation oncology comes from clinical research, registries, and routine clinical data, but the latter has the most patients and features while also having the most missing data.
- Models are developed using a hypothesis-driven approach by learning from a training cohort and estimating performance in a validation cohort. Challenges include gaining trust in models, dealing with continuous changes, and addressing barriers to implementing shared decision making.
- Overall, big data and models can improve cancer care by better tailoring treatments to individual patients, but also require overcoming challenges through rapid learning and collaboration across institutions.
This document is the table of contents for Volume 100 of the journal Sensors & Transducers from January 2009. It lists the editor-in-chief and regional editors, as well as the editorial advisory board which includes over 150 scientists and engineers from around the world with expertise in sensors and transducers.
A Novel Hall Effect Sensor Using Elaborate Offset Cancellation Methodpetousis
This document provides information about the editorial board and editors of the journal Sensors & Transducers. It lists the editor-in-chief, editors for different world regions, and an extensive editorial advisory board with over 150 members from universities and research institutions around the world specializing in sensor technology.
O documento discute como a maioria das pessoas vive distraída das questões espirituais, preferindo as ilusões do mundo material. Muitos religiosos também se acomodam e oferecem promessas simplistas em vez de incentivar o amor ao próximo e a transformação interior. Eventualmente, todos serão forçados a enfrentar a luz espiritual que os levará à fé racional e à convicção da vida imortal.
University of Toronto - Radiomics for Oncology - 2017Andre Dekker
This document contains the slides from a lecture on radiomics for oncology given by Andre Dekker. The lecture covers the rationale for radiomics, which is to use quantitative features extracted from medical images to help predict outcomes like tumor behavior, survival, and response to treatment using machine learning. The major workflow steps of radiomics are discussed, from image acquisition and feature extraction to modeling and validation. Key challenges like robust segmentation and feature reproducibility are also addressed. New directions for radiomics research include applications in preclinical studies, other modalities like PET and MRI, and linking radiomic features to genomic data. Overall, radiomics holds promise to help personalized medicine but large amounts of standardized data are still needed for proper validation of models.
Public Databases for Radiomics Research: Current Status and Future DirectionsCancerImagingInforma
This document discusses radiomics research and public databases. It describes what radiomics is and why data sharing is important. Several public databases are mentioned, with an in-depth look at The Cancer Imaging Archive (TCIA). TCIA hosts radiology data like CT, MR, PET images along with associated data. It provides services to upload and access data and enables data citation. Future directions discussed include standardization initiatives and using cloud computing.
The document discusses deep learning applications for medical image analysis, including for diagnosis, surgical planning and guidance, and risk assessment. Specifically, it presents examples of using deep learning for tasks like classification, segmentation, detection, and pose estimation using medical images from modalities like ultrasound, X-ray, and video. Challenges in the field include limited datasets, variability in medical images, and privacy concerns, but deep learning methods are able to learn features directly from data to help with complex medical image analysis problems.
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
Felipe Campos Kitamura is a medical doctor, radiologist, and AI practitioner whose research interests include medical imaging, computer vision, artificial intelligence, and machine learning. He is currently focused on using machine learning in healthcare applications such as medical imaging analysis and using AI to help summarize surgical events in real-time. Machine learning can be applied in healthcare for tasks like medical diagnosis, predictive analytics for disease screening and monitoring, and assisting with surgical procedures.
Imaging biobanks, report from the european society of radiology Emanuele Neri
In March 2014 the European Society of Radiology (ESR) established a dedicated working group (ESR WG on Imaging Biobanks) aimed at monitoring the existing imaging biobanks in Europe, promoting the federation of imaging biobanks and
communication of their findings in a white paper. The WG provided the following statements:
Imaging biobanks can be defined as “organised databases of medical images and associated imaging biomarkers (radiology and beyond) shared among multiple researchers, and linked to other biorepositories”.
The immediate purpose of imaging biobanks should be to allow the generation of imaging biomarkers for use in research studies and to support biological validation of existing and novel imaging biomarkers.
A long-term scope of imaging biobanks should be the creation of a network/federation of such repositories integrated with the already existing biobanking network.
DiaMe: IoMT deep predictive model based on threshold aware region growing tec...IJECEIAES
Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.
Comparative Study on Cancer Images using Watershed Transformationijtsrd
Digital images are exceptionally huge in the medical image diagnosis frameworks. Image analysis and segmentation are very important tasks in the medical image processing particularly in the field of CAD systems. Visual inspection requires being clear in diagnosis process where the correct region which is affected, need to be separated. Medical imaging plays a very crucial role in all stages of the medical decision process. There are various medical imaging modalities in which mammography are used to detect breast cancer where as MRI for brain tumor and CT for lung cancer. The objective of this paper is to compare the cancer images with different modalities using watershed transformation using metrics. M. Najela Fathin | Dr. S. Shajun Nisha | Dr. M. Mohamed Sathik"Comparative Study on Cancer Images using Watershed Transformation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12767.pdf http://www.ijtsrd.com/computer-science/other/12767/comparative-study-on-cancer-images-using-watershed-transformation/m-najela-fathin
Pathomics Based Biomarkers and Precision MedicineJoel Saltz
Role of Digital Pathology Data Science (Pathomics) in precision medicine. Features from billions or trillions of objects segmented from digital Pathology data can be employed to predict patient outcome and steer treatment.
Presentation at Imaging 2020, Jackson Hole, WY September 2016
3D printing offers benefits for healthcare such as rapid prototyping, mass customization, and ability to create complex geometries that cannot be made through other methods. However, it also faces limitations including being time consuming for all but rare cases, limited material selection, size limitations, and lack of creative uses. Areas that have seen success with 3D printing include prosthetics/exoskeletons through improved fitting, design features, aesthetics and cost reduction. It also shows promise for pre-surgical applications like planning, education, training and surgical guides as well as fields like bioprinting, tissue engineering, and medical education. For 3D printing to reach its potential in healthcare, reimbursement, regulatory approval,
Advances in automatic tuberculosis detection in chest x ray imagessipij
Tuberculosis (TB) is very dangerous and rapidly spread disease in the world. In the investigating cases for
suspected tuberculosis (TB), chest radiography is not only the key techniques of diagnosis based on the
medical imaging but also the diagnostic radiology. So, Computer aided diagnosis (CAD) has been popular
and many researchers are interested in this research areas and different approaches have been proposed
for the TB detection and lung decease classification. In this paper, the medical background history of TB
decease in chest X-rays and a survey of the various approaches in TB detection and classification are
presented. The literature in the related methods is surveyed papers in this research area until now 2014.
On March 23, 2016, Prof. Henning Müller (HES-SO Valais-Wallis and Martinos Center) presented Medical image analysis and big data evaluation infrastructures at Stanford medicine.
Pathomics Based Biomarkers, Tools, and Methodsimgcommcall
This document discusses pathomics-based biomarkers, tools, and methods for multi-scale integrative analysis in biomedical informatics. It summarizes several projects involving extracting quantitative features from pathology and radiology images using image segmentation and analysis techniques. These features are then linked to molecular data and clinical outcomes using statistical and machine learning methods to develop biomarkers. The tools and methods described aim to standardize and optimize feature extraction while accounting for uncertainties.
This document summarizes a seminar on knowledge engineering in oncology given by Andre Dekker. The seminar discusses how big data and machine learning can help address challenges in predicting cancer outcomes and personalizing treatment. It notes that doctors cannot reliably predict outcomes and that data sharing is difficult. However, distributed models that analyze data where it resides without sharing it can help rapidly learn from large numbers of patients. The seminar describes examples of collaborative data networks and predictive models developed for applications like lung cancer stereotactic body radiotherapy and proton therapy patient selection. The goal is developing tools like predictive apps to empower citizens in making healthcare decisions.
ARTIFICIAL NEURAL NETWORKING.
FIRST STEP TO KNOWLEDGE IS TO KNOW THAT we are ignorant
Knowledge in medical field is characterized by uncertanity and vagueness
Historically as well as currently this fact remains a motivation for the development of medical decision support system are based on fuzzy logics
Greek philosopher visualized a basic model of brain function as early as 300 bc
Till date nervous system is not completely understood to human kind.
Dekker trog - radiomics for oncology - 2017Andre Dekker
This document discusses radiomics, which is the process of extracting large amounts of quantitative features from medical images to help diagnose and predict treatment outcomes for cancer patients. It describes the typical radiomics workflow, which involves image segmentation, feature extraction, and building predictive models. It also discusses some challenges in radiomics like accounting for differences in imaging protocols and robust segmentation. The document outlines new directions for radiomics, including applications to preclinical studies, PET imaging, MRI, change over time ("delta radiomics"), combining with genomics ("radiogenomics"), and distributed learning approaches.
Dekker trog - big data for radiation oncology - 2017Andre Dekker
- Big data in radiation oncology comes from clinical research, registries, and routine clinical data, but the latter has the most patients and features while also having the most missing data.
- Models are developed using a hypothesis-driven approach by learning from a training cohort and estimating performance in a validation cohort. Challenges include gaining trust in models, dealing with continuous changes, and addressing barriers to implementing shared decision making.
- Overall, big data and models can improve cancer care by better tailoring treatments to individual patients, but also require overcoming challenges through rapid learning and collaboration across institutions.
This document is the table of contents for Volume 100 of the journal Sensors & Transducers from January 2009. It lists the editor-in-chief and regional editors, as well as the editorial advisory board which includes over 150 scientists and engineers from around the world with expertise in sensors and transducers.
A Novel Hall Effect Sensor Using Elaborate Offset Cancellation Methodpetousis
This document provides information about the editorial board and editors of the journal Sensors & Transducers. It lists the editor-in-chief, editors for different world regions, and an extensive editorial advisory board with over 150 members from universities and research institutions around the world specializing in sensor technology.
O documento discute como a maioria das pessoas vive distraída das questões espirituais, preferindo as ilusões do mundo material. Muitos religiosos também se acomodam e oferecem promessas simplistas em vez de incentivar o amor ao próximo e a transformação interior. Eventualmente, todos serão forçados a enfrentar a luz espiritual que os levará à fé racional e à convicção da vida imortal.
The document is a script describing two sisters, Dahlia and Cynthia, playing a terrifying game of hide and seek with their deranged mother in a dark attic cupboard. As their mother's menacing footsteps approach and she taunts them by tapping on the cupboard door, the terrified girls try to remain silent and still. Their mother's cruel laughter and the sound of the cupboard locking traps the screaming sisters in darkness.
This curriculum vitae summarizes an individual pursuing a 3-year bachelor's degree in hotel management. It outlines their relevant experience including volunteering at an education seminar in Nepal and a 3-month vocational training. Academically, it lists completing intermediate school in 2012 and matriculation in 2006 in Nepal. Areas of interest include food and beverage service. It provides two references from professors and notes achievements of second position in 2013 and first position in an open dance competition in 2011.
لعبة البيرة تعتبر من أحسن الألعاب الجديدة الموجودة علي الساحة وأيضا اللعبة تعتمد علي سرعتك في التقاط زجاجات العاب بيرة المتساقطة من أعلي اللعبة باستخدام الماوس فقط كل ما عليك هو أنك توجه الماوس نحو زجاجات البيرة المتساقطه داخل الصندوق وكلما أيضا جمعت عدد اكبر كلما انتقلت الي مرحلة أعلي وتكون فيها تساقط الزجاجات أسرع من الاول بكتير ولكن احذر فانت لديك ثلاثة محاولات تفلت منك فيها الزجاجات بعد ذلك تخسر لعبة البيرة والنسوان وتظهر لك جملة جيم أوفر .
http://www.download-al3ab.com/2013/08/beera-game-online-2014.html
The document provides information about the SPIE Medical Imaging conference to be held February 15-20, 2014 in San Diego, California. It calls for submissions of abstracts by August 12, 2013 on topics related to medical imaging technologies and their biomedical applications. The conference will cover all aspects of medical imaging including physics, image processing, computer-aided diagnosis, image-guided procedures, and various imaging modalities. Authors are encouraged to present their latest research on imaging physics, systems, applications, and image analysis.
American Association for Cancer Research Annual Meeting 2022
Analysis of images of routinely acquired tissue specimens promise to provide biomarkers that can be used to predict disease outcome and steer treatment, improve diagnostic reproducibility, and reveal new insights to further advance current human understanding of disease. The advent of AI and ubiquitous high-end computing are making it possible to carry out accurate whole slide image morphological and molecular tissue analyses at cellular and subcellular resolutions. AI methods are can enable exploration and discovery of novel diagnostic biomarkers grounded in prognostically predictive spatial and molecular patterns as well as quantitative assessments of predictive value and reproducibility of traditional morphological patterns employed in anatomic pathology. AI methods may be adapted to help steer treatment through integrative analysis of clinical information along with Pathology, Radiology and molecular data.
Fundamentals and Innovations in medical imaging.pptxHemant Rakesh
1. Medical imaging plays a key role in healthcare but errors can be life threatening or fatal, costing billions per year.
2. Artificial intelligence techniques like deep learning can help reduce errors by automating tasks like lesion detection, image segmentation, report generation from medical scans.
3. However, developing accurate AI systems requires large curated datasets which are currently lacking due to privacy and ethical concerns.
The IEEE Engineering in Medicine and Biology Society (EMBS) provides resources for students interested in bioengineering. It has over 10,000 members across 161 chapters globally. The EMBS mission is to advance biomedical engineering applications and provide leadership. Resources for students include funding for chapter activities, paper competitions, summer schools, and awards. The most important resource for students is their time to explore and connect within the EMBS community.
The Engineering in Medicine and Biology Society of the IEEE advances the application of engineering sciences and technology to medicine and biology, promotes the profession, and provides global leadership for the benefit of its members and humanity by disseminating knowledge, setting standards, fostering professional development, and recognizing excellence.
The document discusses opportunities for using medical imaging technologies in business applications. It describes an event hosted by the Space IDEAS Hub at the University of Leicester that focuses on exploring how expertise from space missions can benefit UK industry. The event includes sessions on using space missions for medical imaging, image analysis in pathology, tools for predictive drug screening, and stratified medicine in the UK. The Space IDEAS Hub seeks to transfer technologies and experience from space missions to UK companies for commercial applications.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
I surveyed the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
This document proposes a system to identify bone fractures using x-ray images. It discusses existing fracture detection systems, the advantages and disadvantages of current approaches, and introduces a proposed ensemble model using multiple classifiers and image features. The proposed system would collect x-ray images, augment the data using transformations, and classify images as healthy or fractured bone using deep convolutional neural networks. It aims to automatically detect fractures with high accuracy to help address the limitations of manual diagnosis.
Classification of pathologies on digital chest radiographs using machine lear...IJECEIAES
This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using preprocessed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
Samir Kumar Biswas is an ultrasound and optical physicist/engineer seeking a challenging position in biomedical engineering. He has experience developing photoacoustic and ultrasound imaging systems and has published papers on topics like diffuse optical tomography, angiogenesis monitoring, and rheumatoid arthritis diagnosis. He holds a PhD from the Indian Institute of Science and has held research positions at NUS and the University of Twente.
Important Aspects of Digital Pathology- A Focus on Whole Slide Imaging/Tissue...The Lifesciences Magazine
Applications of Digital Pathology, WSI, and Tissue Image Analysis: 1. Clinical Diagnostics 2. Medical Education 3. Research and Drug Development 4. Telepathology
1) The document discusses the use of artificial intelligence in orthodontics, including applications like automated cephalometric analysis, skeletal classification, predicting orthodontic treatment needs, and 3D tooth segmentation.
2) AI technologies like convolutional neural networks, artificial neural networks, and deep learning are being used in these orthodontic applications.
3) While AI is proving accurate and can help practitioners make decisions faster, limitations include cost, data protection concerns, and ensuring AI systems do not replace human clinicians for serious medical decisions.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
Presentation at Pathology Visions 2017 - https://digitalpathologyassociation.org/2017-pathology-visions-agenda
I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
This document describes a symposium on surgical robotics held on March 28, 2015 in Boston. The symposium brought together surgeons, engineers, and thought leaders to discuss cutting-edge ideas and research in surgical robotics with the goal of sparking innovation. Talks covered topics like government priorities in robotics, tissue modeling and steering. A poster session provided opportunities to learn about research in areas like the robotic-surgeon interface and using robots in clinical practice. The symposium aimed to define grand challenges in surgery and robotics.
Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.
The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.
MseqDR consortium: a grass-roots effort to establish a global resource aimed ...Human Variome Project
The success of whole exome sequencing (WES) for highly heterogeneous disorders, such as mitochondrial disease, is limited by substantial technical and bioinformatics challenges to correctly identify and prioritize the extensive number of sequence variants present in each patient. The likelihood of success can be greatly improved if a large cohort of patient data is assembled in which sequence variants can be systematically analysed, annotated, and interpreted relative to known phenotype. This effort has engaged and united more than 100 international mitochondrial clinicians, researchers, and bioinformaticians in the Mitochondrial Disease Sequence Data Resource (MSeqDR) consortium that formed in June 2012 to identify and prioritize the specific WES data analysis needs of the global mitochondrial disease community. Through regular web-based meetings, we have familiarized ourselves with existing strengths and gaps facing integration of MSeqDR with public resources, as well as the major practical, technical, and ethical challenges that must be overcome to create a sustainable data resource. We have now moved forward toward our common goal by establishing a central data resource (http://mseqdr.org/) that has both public access and secure web-based features that allow the coherent compilation, organization, annotation, and analysis of WES and mtDNA genome data sets generated in both clinical- and research-based settings of suspected mitochondrial disease patients. The most important aims of the MSeqDR consortium are summarized in the MSeqDR portal within the Consortium overview sections. Consortium participants are organized in 3 working groups that include (1) Technology and Bioinformatics; (2) Phenotyping, databasing, IRB concerns and access; and (3) Mitochondrial DNA specific concerns. The online MSeqDR resource is organized into discrete sections to facilitate data deposition and common reannotation, data visualization, data set mining, and access management. With the support of the United Mitochondrial Disease Foundation (UMDF) and the NINDS/NICHD U54 supported North American Mitochondrial Disease Consortium (NAMDC), the MSeqDR prototype has been built. Current major components include common data upload and reannotation using a novel HBCR based annotation tool that has also been made publicly available through the website, MSeqDR GBrowse that allows ready visualization of all public and MSeqDR specific data including labspecific aggregate data visualization tracks, MSeqDR-LSDB instance of nearly 1250 mitochondrial disease and mitochodnrial localized genes that is based on the Locus Specific Database model, exome data set mining in individuals or families using the GEM.app tool, and Account & Access Management. Within MSeqDR GBrowse it is now possible to explore data derived from MitoMap, HmtDB, ClinVar, UCSC-NumtS, ENCODE, 1000 genomes, and many other resources that bioinformaticians recruited to the project are organizing.
1. Researchers developed an X-ray disease identifier using a deep learning model to analyze chest X-ray images and diagnose diseases.
2. They used the VGG19 classification model to process X-ray images from the NIH dataset and diagnose diseases, achieving over 60% accuracy for most diseases.
3. The system aims to assist radiologists by providing automated disease diagnoses from X-ray images to reduce their workload and enable diagnoses in remote areas.
In this work, we describe the field research, design, and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described here are threefold: 1) The design of an advanced visual interface for multimodal diagnosis of breast cancer (BreastScreening); 2) Insights from the field comparison of Single-Modality vs Multi-Modality screening of breast cancer diagnosis with 31 clinicians and 566 images; and 3) The visualization of the two main types of breast lesions in the following image modalities: (i) MammoGraphy (MG) in both Craniocaudal (CC) and Mediolateral oblique (MLO) views; (ii) UltraSound (US); and (iii) Magnetic Resonance Imaging (MRI).
Autodesk 3ds Max Design is used to render and simulate lighting for 3D models exported from AutoCAD. The tutorial demonstrates how to import a model into 3ds Max Design, apply materials and lighting, set up cameras, and generate animations for walkthroughs. Key steps include applying sun lighting and materials to buildings, positioning cameras and lights, and creating an animated fly-through by moving the camera between set key frames.
Analysis of microscope images_FINAL PRESENTATIONGeorge Livanos
This document outlines the presentation scheme for a thesis on the analysis of microscope images. The thesis will analyze tissue samples using both polarimetric imaging at a macroscopic level and microscope imaging at a cellular level. For polarimetric imaging, the thesis will develop statistical models to characterize tissue properties based on how polarized light interacts with tissue elements. For microscope imaging, it will automatically segment cells from immunohistochemistry images and evaluate biomarkers like Her2 to characterize cancer impacts. Key techniques will include membrane boundary estimation, image clustering, and watershed transforms. The goal is both material characterization from polarimetric signatures and cancer analysis from cellular-level microscope images.
1. Website: http://ist2014.ieee-ims.org/
IEEE is the world’s largest professional association, with nearly 500,000 members, dedicated to advancing
technological innovation and excellence for the benefits of humanity. IEEE creates and promotes advancement of
knowledge and world-changing technologies from computing and aerospace, to medical devices, healthcare,
telemedicine, communications, sustainable energy systems, nanotechnology, robotics, and more.
The IEEE International School of Imaging (I2SI) will take place the beautiful island of Santorini, Greece, in
October 14, 2014. The objectives of the IEEE International School of Imaging (I2SI) is to explore physical,
engineering, molecular, biochemical and imaging principles, aimed to the advancement and generation of new
knowledge related to the design, development, and applications of imaging and spectroscopy technologies,
medical diagnostics, pharmaco-imaging, molecular and omics technologies, remote sensing, robotics, space
instrumentation, and material characterization.
Engineers, scientists and medical professionals from Industry, Government, Academia, and Healthcare who want
to bridge technology and clinical disciplines in the multidisciplinary areas of imaging, spectroscopy and medical
diagnostic device industry, are invited to attend the School and interact with major worldwide experts, aimed at
advancing the science of imaging, the development of novel visualization technologies, to increase the
understanding of pathophysiology and metabolism and measure therapeutic efficacy of drugs; remote sensing,
ladars, lidars, space instrumentation, semiconductor inspection, material characterization; exploring multifaceted
design principles and new applications of imaging that would lead ultimately to novel devices and technologies,
standards and metrology with unsurpassable image quality, scalability, reconfigurability, high throughout, and
miniaturization capabilities.
At this stage, the IEEE International School of Imaging focuses on the following four areas:
Medical Diagnostics, Theranostics, Pharmaco-Imaging in Drugs and Medicine
• Medical diagnostics
• Translational theranostics
• Pharmaco-imaging in drugs and medicine
• Bioinformatics
• Neuroimaging
• Robotics, and surgical guidance imaging
• Molecular imaging and biology
• Nanooncology and nanoscale flow-mediated medicine tumors strategies
• Biomarkers, metabolites, omics and translational imaging and spectroscopy
• Image processing and pattern recognition
• Miniaturization of diagnostic and analytical devices
IEEE INTERNATIONAL SCHOOL OF IMAGING (I2SI)
El Greco Resort, Island of Santorini, Greece
Preliminary Program
October 14-16, 2014
In conjunction with IEEE International Conference on Imaging Systems and
Techniques (IST)
Sponsored by the TC-19 Technical Committee on Imaging Systems
I2SI
2. Imaging Modalities and Techniques
• Cameras, microscopy and displays
• Polarimetry, multispectral imaging
• Immunohistochemical Digital Imaging
• MRI, PET, SPECT, CT, ECT Tomography
• Photoacoustic Molecular Imaging
• Omics imaging
• Translational Clinical Imaging Research
• Microwave imaging and inverse scattering
• Ultrasound and laser acoustics
• Multimodality Imaging
• Energy harvesting and imaging technologies
• Emerging imaging trends
Remote Sensing, Space Defense and Commercialization of Space
• Defense and space surveillance imaging technologies
• Automatic Target Recognition (ATR)
• Advanced space instruments and satellite imaging
• Remote Sensing, ladars, and lidars
• Sensors and sensor systems for aerospace applications
• Image processing and pattern recognition
Visualization, Inspection and Manufacturing
• Semiconductor wafers, nanomaterials and composites
• Biomaterial, bionanocomposite characterization
• Sensors and image acquisition
• Illumination architectures
• In-line inspection rapid, whole wafer defect detection
• Off-line inspection for defect review and failure analysis,
• Techniques for critical dimension (CD) and overlay metrology
• Automatic defect classification
• Pharmaceutical and food processing vision Inspection Systems
• Image processing and pattern recognition
Director of the School, and Director, USA
George Giakos, Manhattan College, NY, USA
Co-directors and Directors, Asia
Lihui Peng, Tsinghua University, China
Edmund Lam, University of Hong Kong, China
Wuqiang Yang, University of Manchester, UK
Lijun Xu, Beihang University, China
Co-directors and Directors, Europe
Konstantina Nikita, National Technical University of
Athens, Greece
Nikos Paragios, Ecole Centrale de Paris & Ecole
des Ponts-Paris Tech, France
Mihalis Zervakis, Technical University of Crete,
Greece
Codirector and Director, South America
Jacob Scharcanski, Federal University of Rio
Grande do Sul (UFRGS), Brazil
Technical Program Director
George Giakos, Manhattan College, USA
Technical Program Co-Directors
Stavroula Mougiakakou, University of Bern,
Switzerland
Konstantinos Michmizos, Harvard Medical School,
USA
Nicolas A. Karakatsanis, University of Geneva,
Switzerland
Aditi Deshpande, University of Akron, USA
Technical Coordinators
Tannaz Farrahi, University of Akron, USA
George Livanos, Technical University of Crete,
Greece
Aditi Deshpande, University of Akron, USA
Industrial Relationships/Sponsorships
Coordinator
Bo Liu, University of Akron, USA
Yinan Li, University of Akron, USA
School Administrator
Chris Dyer, Conference Catalysts, LLC, USA
3. Steering Committee
Sos Agaian, The University of Texas/
The University of Texas Health Science Center (USA)
Aggelos Katsaggelos, Northwestern University (USA)
Richard Picard, ARCON Corporation (USA)
Cesare Svelto, Polytechnic of Milan (Italy)
Qionghai Dai, Tsinghua University (China)
James Basilion, Case Western Reserve University (USA)
Alamgir Karim, University of Akron (USA)
Matteo Pastorino, University of Genoa (Italy)
Mel Siegel, Carnegie Mellon University (USA)
Apostolos Georgopoulos, University of Minnesota (USA)
Jin Montclare, NYU Polytechnic School of Engineering (USA)
Xin Yu, Case Western Reserve University (USA)
Emil Petriu, University of Ottawa (Canada)
Xiaolan Deng, China Medical University(China)
Dimitris Metaxas, Rutgers University (USA)
Nikolaos Bourbakis, Wright State University (USA)
Suman Shrestha, University of Massachusetts Medical Center (USA)
Mihalis Zervakis, Technical University of Crete (Greece)
Bing Yu, University of Akron, (USA)
Steven J. Tilden, Everett Charles Technologies, LTX-Credence and Multitest (USA)
George Zentai, Varian Medical Systems (USA)
Sergio Saponara, , University of Pisa (Italy)
Lecturers
Lecture 1: Defining the Cutting Edge: The use of molecular imaging to aid in tumor resections
James P. Basilion, PhD; Professor of Radiology, Biomedical Engineering, and Pathology; Case Western Reserve University; Case
Center for Imaging Research
Lecture 2: Dynamic and parametric whole-body Positron Emission Tomography (PET): A pathway to quantitative
molecular imaging enabling theranostic applications and personalized treatment
Nicolas A. Karakatsanis, PhD; PET Instrumentation and Neuroimaging Laboratory Division of Nuclear Medicine; Hospital Faculty of
Medicine, University of Geneva, Genève, Switzerland
Lecture 3: Instrumentation Design and Techniques in Drug Discovery Imaging and Characterization of the Pharmaceutical
Effects of Drugs
George Giakos, Professor and Chairman, Ph.D, Electrical and Computer Engineering, Manhattan College, NY, USA; Tannaz
Farrahi, Department Of Electrical and Computer Engineering, University of Virginia, USA; Aditi Deshpande, University of Akron,
USA
Lecture 4: New look at Image Quality Assessment and Standardization Methods
Sos Agaian, Ph.D., Professor, The University of Texas Health Science Center, USA
Lecture 5: Electrical Capacitance Processes for Imaging Industrial Processes
Wuqiang Yang, Ph.D., Professor, University of Manchester UK, Lihui Peng, Ph.D., Professor Tsingua University, China; Haigang
Wang, Ph.D., Professor Chinese Academy of Sciences
Lecture 6: Dynamic Contrast Enhanced Imaging
Costas Balas, Ph.D., Professor, TUC/Electronic and Computer Engineering, Greece
Lecture 7: Image construction by using electromagnetic diffracted wavefields: Basic concepts, theory and applications
Matteo Pastorino, Ph.D., Professor, University of Genoa, Italy
Lecture 8: Analytic reconstructions for PET, SPECT, MEG, and EEG
Athanasios Fokas, University of Cambridge, UK; George Katsis, Academy of Athens, Greece
Technical Committees
Image Processing
Nikos Paragios, Ecole Centrale de Paris & Ecole des Ponts-Paris Tech (France)
Sos Agaian, The University of Texas Health Science Center (USA)
Michalis Zervakis, Technical University of Crete (Greece)
Jacob Scharcanski, Federal University of Rio (Brazil)
Pattern Recognition
Aggelos Katsaggelos, Northwestern University (USA)
Nikolaos Bourbakis, Wright State University (USA
4. Cancer Research
James Basilion, Case Western Reserve University (USA)
Imaging Devices and Systems
George Giakos, Manhattan College (USA)
Tannaz Farrahi, University of Virginia (USA)
Lijun Xu Beihang University (China)
Costas Balas, Technical University of Crete (Greece)
Proteonomics, Bionanocomposites
Jin Montclare, NYU Polytechnic School of Engineering (USA)
Omics Imaging/Pharmaceutical Imaging/Drug Characterization
Tannaz Farrahi, University of Virginia (USA)
Xiaolan Deng, China Medical University, (China)
Konstantinos Michmizos, Harvard Medical School, USA
Sos Agaian, The University of Texas Health Science Center (USA)
Jin Montclare, NYU Polytechnic School of Engineering (USA)
ECT
Wuqiang Yang, University of Manchester (UK)
Lihui Peng, Tsinghua University (China)
Remote Sensing/-Super-resolution Imaging
Richard Picard, ARCON Corporation (USA)
Electromagnetics
Matteo Pastorino, University of Genoa (Italy)
Abbas Omar, University of Akron (USA)
Polymer Nanocomposites
Alamgir Karim, Polymer Science, University of Akron (USA)
Medical Imaging
Xi Yu, Case Western Reserve University
Konstantina (Nantia) Nikita, National Technical University of Athens (Greece)
Suman Shrestha, University of Massachusetts Medical Center (USA)
Medical Signals/Neuroimaging
Konstantinos Michmizos, Harvard Medical School (USA)
Stavroula Mougiakakou, University of Bern (Switzerland)
Apostolos Georgopoulos, University of Minnesota (USA)
Dimitris Metaxas, Rutgers University (USA)
Medical Imaging Sensors
George Zentai, Varian Medical systems (USA)
Robotics/Computer Vision
Mel Siegel, Carnegie Melon (USA)
Lasers and Optics
Cesare Svelto, Polytechnic of Milan (Italy)
Signal Processing
Kostas Berberidis, University of Patras (Greece)
Location:
The Greek island of Santorini located in the Aegean sea is one of the most beautiful islands and best travel destinations in the
world. It has beaches with sapphire blue water and vibrant and splendid destinations such as the capital Fira and others like Oia,
Kamari, etc. This picturesque island has all several exciting things to offer travelers and has been named one of the best travel
destinations by Travel & Living magazine and BBC.
5. Venue:
The El Greco resort is one of the most prestigious hotel-resorts in Santorini and has become one of the prime choices for luxury
accommodation. It is situated right beside the capital town of Fira and is minutes away from magnificent beaches and villages of
Monolithos, Kamari and Perissa. El Greco provides various kinds of facilities such as swimming pools, conference center, fitness
center, a restaurant with a beautiful ambience, TV lounge, garden, Wi-fi etc. This resort offers one of the best locations in Santorini
and various facilities, making it the optimum choice for travelers.