Deep learning can be applied to medical imaging to directly extract biomarkers from images or enhance existing biomarkers. It can provide prognostic information beyond diagnosis, such as predicting survival outcomes. Challenges include obtaining sufficient labeled data, handling imbalanced or unlabeled data, and estimating certainty in deep learning decisions. Future work aims to address these issues and define normal populations to identify abnormal data.
June 2018 version
How deep learning reshapes medicine
- Brief deep learning
- Recent applications
- Specific researches
- Perspectives and future directions
This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists.
Pneumonia Classification using Transfer LearningTushar Dalvi
Pneumonia can be life-threatening for people with weak immune systems, in which the alveoli filled with fluid that makes it hard to pass oxygen throughout the bloodstream. Detecting pneumonia is from a chest X-ray is not only expansive but also time-consuming for normal people. Throughout this research introduced a machine learning technique to classify pneumonia from Chest X-ray Images. Most of the medical datasets having class imbalance issues in the dataset. The Data augmentation technique used to reduce the class imbalance from the dataset, Horizontal Flip, width shift and height shift techniques used to complete the augmentation technique. Used VGG19 as a base architecture and ImageNet weights added for the transfer learning approach, also Removing initial layers and adding
some more dense layers helped to discover new possibilities. After testing the proposed model on testing data, we are able to achieve 98% recall and 82% of precision. As compare with state of the art technique, the proposed method able to achieve high
recall but that compromises with Precision.
Developed Project with 3 more colleagues for Pneumonia Detection from Chest X-ray images using Convolutional Neural Network. Used confusion matrix, Recall, Precision for check the model performance on testing Data
June 2018 version
How deep learning reshapes medicine
- Brief deep learning
- Recent applications
- Specific researches
- Perspectives and future directions
This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists.
Pneumonia Classification using Transfer LearningTushar Dalvi
Pneumonia can be life-threatening for people with weak immune systems, in which the alveoli filled with fluid that makes it hard to pass oxygen throughout the bloodstream. Detecting pneumonia is from a chest X-ray is not only expansive but also time-consuming for normal people. Throughout this research introduced a machine learning technique to classify pneumonia from Chest X-ray Images. Most of the medical datasets having class imbalance issues in the dataset. The Data augmentation technique used to reduce the class imbalance from the dataset, Horizontal Flip, width shift and height shift techniques used to complete the augmentation technique. Used VGG19 as a base architecture and ImageNet weights added for the transfer learning approach, also Removing initial layers and adding
some more dense layers helped to discover new possibilities. After testing the proposed model on testing data, we are able to achieve 98% recall and 82% of precision. As compare with state of the art technique, the proposed method able to achieve high
recall but that compromises with Precision.
Developed Project with 3 more colleagues for Pneumonia Detection from Chest X-ray images using Convolutional Neural Network. Used confusion matrix, Recall, Precision for check the model performance on testing Data
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
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.
Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visual...IJECEIAES
The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.
Professor Harrison Bai, Artificial Intelligence Applications in Radiology_mHe...Levi Shapiro
Artificial Intelligence Applications in Radiology, presentation by Dr Harrison Bai, Assistant Professor of Diagnostic Imaging, Warren Alpert Medical School, Brown University. His research interests focus on AI, machine learning, and computer vision as applied to medical image analysis. Dr Bai is an associate editor for the journal Radiology: Artificial Intelligence and is currently a principal investigator for an RSNA Research Scholar grant and an NIH grant. The AI Radiology Lab has various areas of work including COVID-19; Treatment response assessment on imaging (brain, TACE, lung, colorectal); Rapid diagnosis of large-vessel ischemic stroke, patient selection and outcome prediction; Tumor characterization on imaging; Infrastructure development; Federated learning; Image registration (CT-guided tumor ablation); Radiology reports natural language processing. The AI pipeline includes DIANA system, Diagnosis model, severity model and progression model across various automated features and the value proposition. One Technique for dealing with missing sequence and imaging artifact- Sequence dropout. Human-in-the-loop AI. In the short- to mid-term, the utilization of AI needs to be combined with human intervention and supervision. Active learning strategy – annotation. Treatment response evaluation on imaging. Automatic quality estimation to flag the failed cases for humans to review and/or edit. Human in the loop annotation. Automatic quality estimation. Federated learning. Semi-supervised and unsupervised learning. AWS NVIDIA Clara Train SDK using TensorFlow 1.14. Annotations vary across imaging sites. Share weights without sharing data. Domain shift – distribution difference between source data and target data leading to performance degradation.
Glioblastomas brain tumour segmentation based on convolutional neural network...IJECEIAES
Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.
Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN Convolutional Neural Network based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77 85 . gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use. Anagha Jayakumar | Mehtab Mehdi "Brain Tumor Detection using Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38105.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38105/brain-tumor-detection-using-neural-network/anagha-jayakumar
A novel framework for efficient identification of brain cancer region from br...IJECEIAES
Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.
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).
Project report 3D visualization of medical imaging dataShashank
Report of my engineering research on 3D visualisation of medical images obtained from slices of human male and female cadevars. Courtesy NIH (USA), IIIT (Allahabad)
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
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.
Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visual...IJECEIAES
The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.
Professor Harrison Bai, Artificial Intelligence Applications in Radiology_mHe...Levi Shapiro
Artificial Intelligence Applications in Radiology, presentation by Dr Harrison Bai, Assistant Professor of Diagnostic Imaging, Warren Alpert Medical School, Brown University. His research interests focus on AI, machine learning, and computer vision as applied to medical image analysis. Dr Bai is an associate editor for the journal Radiology: Artificial Intelligence and is currently a principal investigator for an RSNA Research Scholar grant and an NIH grant. The AI Radiology Lab has various areas of work including COVID-19; Treatment response assessment on imaging (brain, TACE, lung, colorectal); Rapid diagnosis of large-vessel ischemic stroke, patient selection and outcome prediction; Tumor characterization on imaging; Infrastructure development; Federated learning; Image registration (CT-guided tumor ablation); Radiology reports natural language processing. The AI pipeline includes DIANA system, Diagnosis model, severity model and progression model across various automated features and the value proposition. One Technique for dealing with missing sequence and imaging artifact- Sequence dropout. Human-in-the-loop AI. In the short- to mid-term, the utilization of AI needs to be combined with human intervention and supervision. Active learning strategy – annotation. Treatment response evaluation on imaging. Automatic quality estimation to flag the failed cases for humans to review and/or edit. Human in the loop annotation. Automatic quality estimation. Federated learning. Semi-supervised and unsupervised learning. AWS NVIDIA Clara Train SDK using TensorFlow 1.14. Annotations vary across imaging sites. Share weights without sharing data. Domain shift – distribution difference between source data and target data leading to performance degradation.
Glioblastomas brain tumour segmentation based on convolutional neural network...IJECEIAES
Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.
Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN Convolutional Neural Network based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77 85 . gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use. Anagha Jayakumar | Mehtab Mehdi "Brain Tumor Detection using Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38105.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38105/brain-tumor-detection-using-neural-network/anagha-jayakumar
A novel framework for efficient identification of brain cancer region from br...IJECEIAES
Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.
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).
Project report 3D visualization of medical imaging dataShashank
Report of my engineering research on 3D visualisation of medical images obtained from slices of human male and female cadevars. Courtesy NIH (USA), IIIT (Allahabad)
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
tranSMART Community Meeting 5-7 Nov 13 - Session 2: Creating a Comprehensive ...David Peyruc
tranSMART Community Meeting 5-7 Nov 13 - Session 2: Creating a Comprehensive Clinical and 'Omics Information Commons on Autism
Paul Avillach, Harvard University
The Personalized Health Risk Profile: A New Tool for Safety and Occupational ...Richard Hartman, Ph.D.
This presentation introduces the Personalized Risk Health Profile (PRHP), a mathematical process to quantitatively evaluate personalized health risks by integrating workplace, lifestyle, and environmental exposure (the root cause of disease) data from traditional and new personal monitoring technologies combined with individual health histories and genomic data to provide a new and novel capability for the safety and health professionals and policymakers. The PRHP creates for the first time a mechanism to better understand the relationships between a worker's health, genetic predispositions, and exposures through mathematical expression and process, ultimately providing a modern tool to better understand the effects of exposures from the workplace, environment, as well as day-to-day activities. More importantly, the PRRP displays individual and population risks through user-friendly visualizations bridging the gap between "Population Health" and "Personalized Medicine" so safety and health professionals can recommend data-driven interventions to mitigate individual risks to improve health/performance, and policymakers and decision-makers can make more informed policy and resource decisions.
Exploiting biomedical literature to mine out a large multimodal dataset of rare cancer studies. Presentation of Anjani K. Dhrangadhariya (Institute of Information Systems, HES-SO Valais-Wallis, Sierre) at SPIE Medical Imaging 2020.
Basics of Data Analysis in BioinformaticsElena Sügis
Presentation gives introduction to the Basics of Data Analysis in Bioinformatics.
The following topics are covered:
Data acquisition
Data summary(selecting the needed column/rows from the file and showing basic descriptive statistics)
Preprocessing (missing values imputation, data normalization, etc.)
Principal Component Analysis
Data Clustering and cluster annotation (k-means, hierarchical)
Cluster annotations
Slides de la conférence conjointe CORIA-TALN 2018 qui s'est déroulé du 14 au 18 mai 2018 à Rennes.
Keynote par Dina Demner-Fushman le jeudi 17 mai 2018.
Forum on Personalized Medicine: Challenges for the next decadeJoaquin Dopazo
Bioinformatics and Big Data in the era of Personalized Medicine
10th Anniversary Instituto Roche Forum on Personalized Medicine: Challenges for the next decade.
Santiago de Compostela (Spain), September 25th 2014
In this talk, we present our work on developing large-scale text mining and machine learning tools as well as their uses in real-world applications in PubMed search, biocuration and healthcare (medical image analysis).
DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resour...Anjani Dhrangadhariya
PICO recognition is an information extraction task for identifying participant, intervention, comparator, and outcome information from clinical literature.
Manually identifying PICO information is the most time-consuming step for conducting systematic reviews (SR) which is already a labor-intensive process.
A lack of diversified and large, annotated corpora restricts innovation and adoption of automated PICO recognition systems.
The largest-available PICO entity/span corpus is manually annotated which is too expensive for a majority of the scientific community.
To break through the bottleneck, we propose DISTANT-CTO, a novel distantly supervised PICO entity extraction approach using the clinical trials literature, to generate a massive weakly-labeled dataset with more than a million ``Intervention'' and ``Comparator'' entity annotations.
We train distant NER (named-entity recognition) models using this weakly-labeled dataset and demonstrate that it outperforms even the sophisticated models trained on the manually annotated dataset with a 2\% F1 improvement over the Intervention entity of the PICO benchmark and more than 5\% improvement when combined with the manually annotated dataset.
We investigate the generalizability of our approach and gain an impressive F1 score on another domain-specific PICO benchmark.
The approach is not only zero-cost but is also scalable for a constant stream of PICO entity annotations.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
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TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists
Deep learning application to medical imaging: Perspectives as a physician
1. Deep learning application to
medical imaging:
Perspectives as a physician
Hongyoon Choi
Cheonan Public Health Center
Department of Nuclear Medicine, Seoul National University Hospital
2. CONTENTS
INTRODUCTION
Era of Medical Data Scientists
DEEP LEARNING
BASED BIOMARKER
What clinicians want
PRACTICAL ISSUES &
PERSPECTIVES
What to solve and future directions
3. INTRODUCTION
15% of GDP
Who gets
this
money?
* For USA
Single target
Pharmaceutical
companies
Systemic, multimodal,
multidimensional
Tech companies
Current
Future
10. Deep learning-based biomarker
Disease lesion detection
Lesion segmentation
Lesion classification
Above or equivalent to human experts level
What we eventually want
Whether subjects die?
If then, when?
Appropriate treatment options
What we want to DL is a ‘biomarker’
11. Deep learning-based biomarker
Issues in DL application to biomedical fields
– Various purposes
• More than simple diagnosis: Prognosis, Disease
status monitor, Response prediction.
– Ambiguous ground-truth
• Diagnosis is not a simple classification
12. Deep learning-based biomarker
Clinical diagnosis:
Spectrum instead of
a clear-cut classification
Blood pressure = a type of biomarker
Cardiovascular
eventrisk
140/90
HypertensionNormal
Blood Pressure
(one-dimensional)
Quantitative biomarker
reflecting prognostic outcome
Direct
Most diseases…
Multiple domain and high-dimensional
Not rely on single measurement
13. Deep learning-based biomarker
Brain imaging
Symptoms
Drug responses
Lab Test
• Prognosis:
Whether patient will
have cognitive dysfunction
Deep neural network Quantitative biomarker
reflecting prognostic outcome
Low-dimensional features/ scores
Qualitative/Empirical
(high-dimensional
Multimodal)
14. Deep learning-based biomarker
Direct biomarker: Output of supervised learning
Indirectly enhance conventional biomarker
Biomarker beyond diagnosis
16. Deep learning-based biomarker
• Direct biomarker
– Deep learning: y=f(x) where y: label. x: data
f
P(y|x)
Cat : 0.98
Dog : 0.01
Cotton: 0.00
Real implementation
Get probability label for given data
Direct biomarker
17. Deep learning-based biomarker
• Direct biomarker
– Deep learning: y=f(x) where y: label. x: data
Parkinson 60%
Dementia 10%
In 10 Years
A subject’s future report
Depression 20%
P(y|x)
X: Image
Y: Disease
Direct biomarker
A single parameter
Direct biomarker
18. Deep learning-based biomarker
FDG and amyloid PET to predict future cognitive decline
Normal MCI Alzheimer
Slow progression
Rapid progression
FDG PET
Amyloid PET
AD Normal
But, real case..
Direct biomarker
19. Deep learning-based biomarker
AD & NC
MCI-converter & non-converter
FDG and amyloid PET to predict future cognitive decline
Choi H and Jin KH Arxiv 2017
3-D CNN architecture
Direct biomarker
20. Deep learning-based biomarker
Output score measured by
baseline PET
& 3-year cognitive score
f p(Alzheimer|X)
Direct biomarker
A single parameter
Choi H and Jin KH Arxiv 2017
Direct biomarker
22. Deep learning-based biomarker
SWEDD:
Clinically PD,
Image normal
Expert1 Expert2
Conventional
quantification
SWEDD
Deep learning-
abnormal
2-year follow-up
80%
Abnormal
Deep learning : Redefine SWEDD diagnosis
Choi, H., … ,Lee. D.S. NeuroImage: Clinical 2017
Dopamine transporter imaging for Parkinson’s disease diagnosis
3D CNN model
Direct biomarker
23. Deep learning-based biomarker
Direct biomarker: Output of supervised learning
Indirectly enhance conventional biomarker
Biomarker beyond diagnosis
Enhancing biomarker
24. Deep learning-based biomarker
Enhancing biomarker
Number of reports w.r.t.
DL application to medical imaging
Litjens G et al. Arxiv 2017
Medical Imaging Segmentation
Conventional Biomarkers
- Tumor volume
- Area of necrosis
- Combined with functional images
25. Deep learning-based biomarker
Enhancing biomarker
Conventional image quantification
- Measurement of radiotracer binding
- Requiring accurate segmentation
normal PD
Noninvasive imaging of
dopaminergic degeneration
Combined with Functional
Imaging
26. Deep learning-based biomarker
Brain tissue segmentation
Enhancing biomarker
Choi, H., & Jin, K. H. J Neurosci Methods 2016
de Brebisson, et al. CVPR 2015.
Chen H, et al. Arxiv 2017
28. Deep learning-based biomarker
Image enhancement for better biomarker acquisition
Enhancing biomarker
Normal dose abdomen CT Low dose abdomen CT Low dose abdomen CT+CNN
Chen H et al. Biomed Opt Exp 2017
Standard dose
PET
Low dose
PET
Low dose
PET + CNN Xiang L, et al. Neurocomputing, 2017
29. Deep learning-based biomarker
• Image Generation
Enhancing biomarker
Discriminative
features
Features
Common deep learning model Generative model
z = f(x)
where x: data, z: discriminative features
f: classifier model
x = g(z)
where x: data, z: latent
g: generation function
30. Deep learning-based biomarker
• Image Generation - Autoencoder
Enhancing biomarker
X
f:
encoder
z
g:
decoder
X
X=g(f(X))
X
f:
encoder
μ, σ
Regularized latent features
z~N(0,1)
g:
decoderμ+σZ
X
Variational autoencoder
31. Deep learning-based biomarker
• Image Generation – Generative adversarial network
Enhancing biomarker
z~N(0,1)
G:
generator
Fake image
Real image
D:
Discriminator
1: real
0: fake
32. Deep learning-based biomarker
• Image Generation – Generative adversarial network
Enhancing biomarker
z~N(0,1)
G
Fake image
Real image
D 1: real
0: fake
Training D
z~N(0,1)
G
Fake image
D Fake,
But 1
Training G
33. Deep learning-based biomarker
• Image Generation – Generative adversarial network
Enhancing biomarker
Structural MR generation from PET
Florbetapir PET
Generator:
U-net
Skip connection
Generated MR
PETandgeneratedMRPETandrealMR
Discriminator Real or Fake
Generative Adversarial Networks
for MR generation
z G(z)
z & G(z)
z & x
Choi H and Lee DS, J Nucl Med 2017.
34. Deep learning-based biomarker
Direct biomarker: Output of supervised learning
Indirectly enhance conventional biomarker
Biomarker beyond diagnosis
Enhancing biomarker
35. Deep learning-based biomarker
Beyond diagnosis
Most studies focuses on
‘classification’
Y = f(X) where f: classifier
Real world
Whether subjects die?
If then, when?
Appropriate treatment options
Clinically, really require
‘special regression’
Y = f(X) where f: regressor
Diagnosis Prognosis
36. Deep learning-based biomarker
Beyond diagnosis
Deep learning and survival data
Y~ f(X)
Y Target
Model f trained by minimize
L2 distance(MSE) between Y and f(X)
Prognosis
Common regression Regression of Survival Data
Time and events
(Labeled data:
For a subject i, death or survived at
time Ti )
A model estimates risk function, h(X)
Where hazard function at time t,
λ(t,x) = λ0(t) exp (h(x))
37. Deep learning-based biomarker
Beyond diagnosis
Deep learning and survival data
Prognosis
λ(t,x) = λ0(t) exp (h(x))
h(x) = Σ βX for cox linear regression
h(X) = f(X) where, f : neural network model.
Training target : maximize hi(X) at death time Ti
and minimize hj(X) other subjects still at risk (i.e. survival at Time Ti)
Katzmann JL, et al. Arxiv 2016.
38. Deep learning-based biomarker
Beyond diagnosis
Low risk group
High risk group
Application to transcriptome data of lung cancer
Deep learning-based risk score
Choi H and Na KJ, Biomed Res Int. In Press
39. Deep learning-based biomarker
Estimating normal population distribution
- Disease : defined by distance from normal
- Abnormal data are not always available
- Particularly for rare disorders
Beyond diagnosis
Define normal is important
40. Deep learning-based biomarker
X
f:
encoder
μ, σ
g:
decoderμ+σZ
X
Variational autoencoder
Beyond diagnosis
+y : Conditional input (covariates)
Conditional variational autoencoder
X
Y: Cat
f:
encoder
μ, σ
μ+σZ
Y: Cat
g:
decoder
X
42. Deep learning-based biomarker
• Individual vs population
– Aging of individual brain : Comparing with virtual population
Population distribution of brain
metabolism at each age by iterative
generating from the VAE model
Generator
Latent
features
N(0,1) Random sampling
from normal distribution
+ Age
Age
50
55
60
65
70
75
• Evaluating individual brain’s aging
compared with general population
• Define ‘pathologic aging’
Choi H,… Lee DS. Biorxiv 2017
Beyond diagnosis
43. Deep learning-based biomarker
Direct biomarker
Indirectly enhance conventional biomarker
Biomarker beyond diagnosis
Use p(Y|X) as a single parameter
Segmentation/Image enhancement/Image generation
Targets different from simple classification/
Define normal population
44. Practical issues & Perspectives
Where to get
Hospital
• Best source
• Practical model validation: Real world
problems
• Ethical issues and relatively small data size
Public database
• Easy to get
• Red ocean
• Limitation in real-world validation
46. Practical issues & Perspectives
Data Size
– Does deep learning require big data?
– Always more than 100,000 images?
Answer : No
Answer : Yes
47. Practical issues & Perspectives
Data Size
– Voxel-based training:
Segmentation/Superresolution etc.
– Image generation (Pix2Pix)
:~100 3d volumes
– Image augmentation
: rotation/flipping
(but, cautious. Need to consult to clinician)
– Task and complexity dependent:
AD vs normal was trained by ~300 cases.
48. Practical issues & Perspectives
Image labels
Combined with natural language processing
49. Practical issues & Perspectives
Image labels
– Unlabeled data >> Labeled data @ Hospital
– Unbalanced label
Importance of unsupervised & semi-
supervised learning
50. Practical issues & Perspectives
Certainty of DL-based decision
– Recently combined with Bayesian
approximation
– Estimate p(θ|X,Y) (θ – model, X: image, Y: label)
C Leibig, et al. biorxiv 2017
• Bayesian neural network
• Dropout as a Bayesian approximation
51. Practical issues & Perspectives
Current decision
Rule-base, Decision Tree
52. Practical issues & Perspectives
Future medical decision
ConcatenatingFeatures
• Diagnosis
• Management Plan
ex) Operation or chemotherapy
140/90
AbnormalNormal
Integrated biomarker based on DL
RiskatDeath
53. Take Home Messages
• Era of medical data scientists
– Enormous roles of math/stats/c.s.
• What clinician want?
– DL aims at the most use of data and extract the best
discriminative biomarker (feature)
– DL can directly extract biomarker or help extract conventional
biomarker
– Beyond classification: Prognosis and defining normal
• Future direction
– Many things to solve
(e.g. unsupervised, overcome small/unlabeled data, uncertainty)
