Session by Sabyasachi Mukhopadhyay
Kolkata Lead, Facebook Developer Circle
GDE in ML
Intel Software Innovator
Visiting Faculty, SCIT Pune
Co-Founder & Chief Research Officer, Twelit MedTech Pvt. Ltd
As in many other scientific domains where computer–based tools need to be evaluated, also medical imaging often requires the expensive generation of manual ground truth. For some specific tasks medical doctors can be required to guarantee high quality and valid results, whereas other tasks such as the image modality classification described in this text can in sufficiently high quality be performed with simple domain experts.
Lec4: Pre-Processing Medical Images (II)Ulaş Bağcı
2017 Spring, UCF Medical Image Computing CAVA: Computer Aided Visualization and Analysis • CAD: Computer Aided Diagnosis • Definitions and Terminologies • Coordinate Systems • Pre-Processing Images – Volume of Interest – RegionofInterest – IntensityofInterest – ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Deep Learning of Tissue Specific Speckle Representations in Optical Coherence...Debdoot Sheet
"Deep Learning of Tissue Specific Speckle Representations in Optical Coherence Tomography and Deeper Exploration for In situ Histology" presented at the Deep Learning for Biomedical Imaging session at the 2015 IEEE International Symposium on Biomedical Imaging (ISBI 2015) at Brooklyn, New York, USA.
Trends in deep learning in 2020 - International Journal of Artificial Intelli...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
As in many other scientific domains where computer–based tools need to be evaluated, also medical imaging often requires the expensive generation of manual ground truth. For some specific tasks medical doctors can be required to guarantee high quality and valid results, whereas other tasks such as the image modality classification described in this text can in sufficiently high quality be performed with simple domain experts.
Lec4: Pre-Processing Medical Images (II)Ulaş Bağcı
2017 Spring, UCF Medical Image Computing CAVA: Computer Aided Visualization and Analysis • CAD: Computer Aided Diagnosis • Definitions and Terminologies • Coordinate Systems • Pre-Processing Images – Volume of Interest – RegionofInterest – IntensityofInterest – ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Deep Learning of Tissue Specific Speckle Representations in Optical Coherence...Debdoot Sheet
"Deep Learning of Tissue Specific Speckle Representations in Optical Coherence Tomography and Deeper Exploration for In situ Histology" presented at the Deep Learning for Biomedical Imaging session at the 2015 IEEE International Symposium on Biomedical Imaging (ISBI 2015) at Brooklyn, New York, USA.
Trends in deep learning in 2020 - International Journal of Artificial Intelli...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Lec2: Digital Images and Medical Imaging ModalitiesUlaş Bağcı
2017 Spring, UCF Medical Image Computing Course
X-ray?
• Ultrasound?
• ComputedTomography(CT)?
• MagneticResonanceImaging(MRI)?
• PositronEmissionTomography(PET)? • DiffusionWeightedImaging(DWI)?
• DiffusionTensorImaging(DTI)?
• MagneticParticleImaging(MPI)?
• OpticalCoherenceTomography(OCT)?
Basics of Radiological Image Modalities and their clinical use (MRI, PET, CT, fMRI, DTI, ...) • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Spectral analysis of remotely sensed images provide the required information accurately even for small
targets. Hence Hyperspectral imaging is being used which follows the technique of dividing images into
bands. These Hyperspectral images find their applications in agriculture, biomedical, marine analysis, oil
seeps detection etc. A Hyperspectral image contains many spectra, one for each individual point on the
sample’s surface and in this project the required target on the Hyperspectral image is going to be detected
and classified. Hyperspectral remote sensing image classification is a challenging problem because of its
high dimensional inputs, many class outputs and limited availability of reference data. Therefore some
powerful techniques to improve the accuracy of classification are required. The objective of our project is
to reduce the dimensionality of the Hyperspectral image using Principal Component Analysis followed by
classification using Neural Network. The project is to be implemented using MATLAB.
MRI Brain Image Segmentation using Fuzzy Clustering Algorithmsijtsrd
MR image segmentation assumes a significant job and a significant job in the restorative field because of its assortment of utilizations particularly in Brain tumor investigation. Cerebrum tumor is an unusual and uncontrolled development of cells. It occupies room inside the skull. It can pack, move and damage solid cerebrum tissue and nerves. Additionally as a rule it deter with ordinary mind work. Tumors can be kindhearted non dangerous or threatening malignant , can occur in various pieces of the cerebrum. Cerebrum tumor arrangement and ID from Magnetic Resonance MR information is a fundamental. However, it requires some serious energy and manual errand finished by restorative pros. Mechanizing this undertaking is a difficult due to the high assortment in the vibe of tumor tissues among various patients and by and large similitude with the ordinary tissues. Right now, tumor picture has been portioned utilizing proposed Fuzzy grouping calculation FCM . The presentation of FCM division strategy is contrasted and those of watershed and SVM calculations. Pavithra. R | E. Sivaraman "MRI Brain Image Segmentation using Fuzzy Clustering Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30316.pdf Paper Url :https://www.ijtsrd.com/engineering/electrical-engineering/30316/mri-brain-image-segmentation-using-fuzzy-clustering-algorithms/pavithra-r
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.
Whispers of Speckles (Part I: Building Computational Imaging Frameworks for ...Debdoot Sheet
Whispers of Speckles
(Part I: Building Computational Imaging Frameworks for Acoustic and Optical Speckle Imaging)
(Part II: Enlightenment from Shallow to Complex Reasoning with Deep Learning)
Presented at the Workshop on Machine Learning for Medical Image Analysis (WMLMIA), IIT Mandi, 25 June 2015.
Visual character n grams for classification and retrieval of radiological imagesijma
Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases
would help the inexperienced radiologist in the interpretation process. Character n-gram model has been
effective in text retrieval context in languages such as Chinese where there are no clear word boundaries.
We propose the use of visual character n-gram model for representation of image for classification and
retrieval purposes. Regions of interests in mammographic images are represented with the character ngram
features. These features are then used as input to back-propagation neural network for classification
of regions into normal and abnormal categories. Experiments on miniMIAS database show that character
n-gram features are useful in classifying the regions into normal and abnormal categories. Promising
classification accuracies are observed (83.33%) for fatty background tissue warranting further
investigation. We argue that Classifying regions of interests would reduce the number of comparisons
necessary for finding similar images from the database and hence would reduce the time required for
retrieval of past similar cases.
Brain tissue segmentation from MR images Tanmay Patil
This presentation was made for an engineering technical seminar in Biomedical engineering branch.
The presentation consist of working of MRI and method for segmenting the brain tissue..
The content was taken from various papers which are given as references at the end of ppt.
Extended Fuzzy Hyperline Segment Neural Network for Fingerprint RecognitionCSCJournals
In this paper we have proposed Extended Fuzzy Hyperline Segment Neural Network (EFHLSNN) and its learning algorithm which is an extension of Fuzzy Hyperline Segment Neural Network (FHLSNN). The fuzzy set hyperline segment is an n-dimensional hyperline segment defined by two end points with a corresponding extended membership function. The fingerprint feature extraction process is based on FingerCode feature extraction technique. The performance of EFHLSNN is verified using POLY U HRF fingerprint database. The EFHLSNN is found superior compared to FHLSNN in generalization, training and recall time.
Investigation of Chaotic-Type Features in Hyperspectral Satellite Datacsandit
Hyperspectral images provide detailed spectral info
rmation with more than several hundred
channels. On the other hand, the high dimensionalit
y in hyperspectral images also causes to
classification problems due to the huge ratio betwe
en the number of training samples and the
features. In this paper, Lyapunov Exponents (LEs) a
re used to determine chaotic-type structure
of EO- 1 Hyperion hyperspectral image, a mixed fore
st site in Turkey. Experimental results
demonstrate that EO-1 Hyperion image has a chaotic
structure by checking distribution of
Lyapunov Exponents (LEs) and they can be used as d
iscriminative features to improve
classification accuracy for hyperspectral images.
Lec2: Digital Images and Medical Imaging ModalitiesUlaş Bağcı
2017 Spring, UCF Medical Image Computing Course
X-ray?
• Ultrasound?
• ComputedTomography(CT)?
• MagneticResonanceImaging(MRI)?
• PositronEmissionTomography(PET)? • DiffusionWeightedImaging(DWI)?
• DiffusionTensorImaging(DTI)?
• MagneticParticleImaging(MPI)?
• OpticalCoherenceTomography(OCT)?
Basics of Radiological Image Modalities and their clinical use (MRI, PET, CT, fMRI, DTI, ...) • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Spectral analysis of remotely sensed images provide the required information accurately even for small
targets. Hence Hyperspectral imaging is being used which follows the technique of dividing images into
bands. These Hyperspectral images find their applications in agriculture, biomedical, marine analysis, oil
seeps detection etc. A Hyperspectral image contains many spectra, one for each individual point on the
sample’s surface and in this project the required target on the Hyperspectral image is going to be detected
and classified. Hyperspectral remote sensing image classification is a challenging problem because of its
high dimensional inputs, many class outputs and limited availability of reference data. Therefore some
powerful techniques to improve the accuracy of classification are required. The objective of our project is
to reduce the dimensionality of the Hyperspectral image using Principal Component Analysis followed by
classification using Neural Network. The project is to be implemented using MATLAB.
MRI Brain Image Segmentation using Fuzzy Clustering Algorithmsijtsrd
MR image segmentation assumes a significant job and a significant job in the restorative field because of its assortment of utilizations particularly in Brain tumor investigation. Cerebrum tumor is an unusual and uncontrolled development of cells. It occupies room inside the skull. It can pack, move and damage solid cerebrum tissue and nerves. Additionally as a rule it deter with ordinary mind work. Tumors can be kindhearted non dangerous or threatening malignant , can occur in various pieces of the cerebrum. Cerebrum tumor arrangement and ID from Magnetic Resonance MR information is a fundamental. However, it requires some serious energy and manual errand finished by restorative pros. Mechanizing this undertaking is a difficult due to the high assortment in the vibe of tumor tissues among various patients and by and large similitude with the ordinary tissues. Right now, tumor picture has been portioned utilizing proposed Fuzzy grouping calculation FCM . The presentation of FCM division strategy is contrasted and those of watershed and SVM calculations. Pavithra. R | E. Sivaraman "MRI Brain Image Segmentation using Fuzzy Clustering Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30316.pdf Paper Url :https://www.ijtsrd.com/engineering/electrical-engineering/30316/mri-brain-image-segmentation-using-fuzzy-clustering-algorithms/pavithra-r
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.
Whispers of Speckles (Part I: Building Computational Imaging Frameworks for ...Debdoot Sheet
Whispers of Speckles
(Part I: Building Computational Imaging Frameworks for Acoustic and Optical Speckle Imaging)
(Part II: Enlightenment from Shallow to Complex Reasoning with Deep Learning)
Presented at the Workshop on Machine Learning for Medical Image Analysis (WMLMIA), IIT Mandi, 25 June 2015.
Visual character n grams for classification and retrieval of radiological imagesijma
Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases
would help the inexperienced radiologist in the interpretation process. Character n-gram model has been
effective in text retrieval context in languages such as Chinese where there are no clear word boundaries.
We propose the use of visual character n-gram model for representation of image for classification and
retrieval purposes. Regions of interests in mammographic images are represented with the character ngram
features. These features are then used as input to back-propagation neural network for classification
of regions into normal and abnormal categories. Experiments on miniMIAS database show that character
n-gram features are useful in classifying the regions into normal and abnormal categories. Promising
classification accuracies are observed (83.33%) for fatty background tissue warranting further
investigation. We argue that Classifying regions of interests would reduce the number of comparisons
necessary for finding similar images from the database and hence would reduce the time required for
retrieval of past similar cases.
Brain tissue segmentation from MR images Tanmay Patil
This presentation was made for an engineering technical seminar in Biomedical engineering branch.
The presentation consist of working of MRI and method for segmenting the brain tissue..
The content was taken from various papers which are given as references at the end of ppt.
Extended Fuzzy Hyperline Segment Neural Network for Fingerprint RecognitionCSCJournals
In this paper we have proposed Extended Fuzzy Hyperline Segment Neural Network (EFHLSNN) and its learning algorithm which is an extension of Fuzzy Hyperline Segment Neural Network (FHLSNN). The fuzzy set hyperline segment is an n-dimensional hyperline segment defined by two end points with a corresponding extended membership function. The fingerprint feature extraction process is based on FingerCode feature extraction technique. The performance of EFHLSNN is verified using POLY U HRF fingerprint database. The EFHLSNN is found superior compared to FHLSNN in generalization, training and recall time.
Investigation of Chaotic-Type Features in Hyperspectral Satellite Datacsandit
Hyperspectral images provide detailed spectral info
rmation with more than several hundred
channels. On the other hand, the high dimensionalit
y in hyperspectral images also causes to
classification problems due to the huge ratio betwe
en the number of training samples and the
features. In this paper, Lyapunov Exponents (LEs) a
re used to determine chaotic-type structure
of EO- 1 Hyperion hyperspectral image, a mixed fore
st site in Turkey. Experimental results
demonstrate that EO-1 Hyperion image has a chaotic
structure by checking distribution of
Lyapunov Exponents (LEs) and they can be used as d
iscriminative features to improve
classification accuracy for hyperspectral images.
A linear-Discriminant-Analysis-Based Approach to Enhance the Performance of F...CSCJournals
Spike sorting is of prime importance in neurophysiology and hence has received considerable attention. However, conventional methods suffer from the degradation of clustering results in the presence of high levels of noise contamination. This paper presents a scheme for taking advantage of automatic clustering and enhancing the feature extraction efficiency, especially for low-SNR spike data. The method employs linear discriminant analysis based on a fuzzy c-means (FCM) algorithm. Simulated spike data [1] were used as the test bed due to better a priori knowledge of the spike signals. Application to both high and low signal-to-noise ratio (SNR) data showed that the proposed method outperforms conventional principal-component analysis (PCA) and FCM algorithm. FCM failed to cluster spikes for low-SNR data. For two discriminative performance indices based on Fisher's discriminant criterion, the proposed approach was over 1.36 times the ratio of between- and within-class variation of PCA for spike data with SNR ranging from 1.5 to 4.5 dB. In conclusion, the proposed scheme is unsupervised and can enhance the performance of fuzzy c-means clustering in spike sorting with low-SNR data.
Gaussian Multi-Scale Feature Disassociation Screening in Tuberculosiseijceronline
Tuberculosis is a major health threat in many regions of the world. When left undiagnosed and consequently untreated, death rates of patients with tuberculosis are high. We first extract the lung region using a lung nodule Edge detection method. For this lung region, we compute a set of texture and shape features, which enable the x-rays to be classified as normal or abnormal using a binary classifier. Thus, a development of edge detection solution to address these requirements can be implemented in a wide range of situations. The general criteria for edge detection includes detection of edge with lower rorrate ,whichmeans that the detection should accurately catch as many edges.
In this paper we present the use of a signal processing technique to find dominant channels in
near infrared spectroscopy (NIRS). Cross correlation is computed to compare measuring
channels and identify delays among the channels. In addition, visual inspection was used to
detect potential dominant channels. The results showed that the visual analysis exposed painrelated
activations in the primary somatosensory cortex (S1) after stimulation which is
consistent with similar studies and the cross correlation analysis found dominant channels on
both cerebral hemispheres. The analysis also showed a relationship between dominant channels
and neighbouring channels. Therefore, our results present a new method to detect dominant
regions in the cerebral cortex using near-infrared spectroscopy. These results have also
implications in the reduction of number of channels by eliminating irrelevant channels for the
experiment.
CROSS CORRELATION ANALYSIS OF MULTI-CHANNEL NEAR INFRARED SPECTROSCOPYcscpconf
In this paper we present the use of a signal processing technique to find dominant channels in near infrared spectroscopy (NIRS). Cross correlation is computed to compare measuring channels and identify delays among the channels. In addition, visual inspection was used to detect potential dominant channels. The results showed that the visual analysis exposed painrelated activations in the primary somatosensory cortex (S1) after stimulation which is consistent with similar studies and the cross correlation analysis found dominant channels on both cerebral hemispheres. The analysis also showed a relationship between dominant channels
and neighbouring channels. Therefore, our results present a new method to detect dominant regions in the cerebral cortex using near-infrared spectroscopy. These results have also implications in the reduction of number of channels by eliminating irrelevant channels for the experiment.
CROSS CORRELATION ANALYSIS OF MULTI-CHANNEL NEAR INFRARED SPECTROSCOPYcscpconf
In this paper we present the use of a signal processing technique to find dominant channels in near infrared spectroscopy (NIRS). Cross correlation is computed to compare measuring channels and identify delays among the channels. In addition, visual inspection was used to detect potential dominant channels. The results showed that the visual analysis exposed painrelated activations in the primary somatosensory cortex (S1) after stimulation which is consistent with similar studies and the cross correlation analysis found dominant channels on both cerebral hemispheres. The analysis also showed a relationship between dominant channels and neighbouring channels. Therefore, our results present a new method to detect dominant regions in the cerebral cortex using near-infrared spectroscopy. These results have also implications in the reduction of number of channels by eliminating irrelevant channels for the experiment
AN EFFICIENT WAVELET BASED FEATURE REDUCTION AND CLASSIFICATION TECHNIQUE FOR...ijcseit
This research paper proposes an improved feature reduction and classification technique to identify mild and severe dementia from brain MRI data. The manual interpretation of changes in brain volume based on visual examination by radiologist or a physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed
which caters the need for classification of brain MRI after identifying abnormal MRI volume, for the diagnosis of dementia. In this research work, advanced classification techniques using Support Vector Machines based on Particle Swarm Optimisation and Genetic algorithm are compared. Feature reduction
by wavelets and PCA are analysed. From this analysis, it is observed that the proposed classification of SVM based PSO is found to be efficient than SVM trained with GA and wavelet based feature reduction technique yields better results than PCA.
AN EFFICIENT WAVELET BASED FEATURE REDUCTION AND CLASSIFICATION TECHNIQUE FOR...ijcseit
This research paper proposes an improved feature reduction and classification technique to identify mild
and severe dementia from brain MRI data. The manual interpretation of changes in brain volume based on
visual examination by radiologist or a physician may lead to missing diagnosis when a large number of
MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed
which caters the need for classification of brain MRI after identifying abnormal MRI volume, for the
diagnosis of dementia. In this research work, advanced classification techniques using Support Vector
Machines based on Particle Swarm Optimisation and Genetic algorithm are compared. Feature reduction
by wavelets and PCA are analysed. From this analysis, it is observed that the proposed classification of
SVM based PSO is found to be efficient than SVM trained with GA and wavelet based feature reduction
technique yields better results than PCA.
The term biophotonics denotes a combination of biology and photonics, with photonics being the science and technology of generation, manipulation, and detection of photons, quantum units of light. Photonics is related to electronics and photons. Photons play a central role in information technologies such as fiber optics the way electrons do in electronics.
Biophotonics can also be described as the "development and application of optical techniques, particularly imaging, to the study of biological molecules, cells and tissue". One of the main benefits of using optical techniques which make up biophotonics is that they preserve the integrity of the biological cells being examined.
Dr. Patrick Bradshaw presents an overview of his program, Sensory Information Systems, at the AFOSR 2013 Spring Review. At this review, Program Officers from AFOSR Technical Divisions will present briefings that highlight basic research programs beneficial to the Air Force.
NIRS-BASED CORTICAL ACTIVATION ANALYSIS BY TEMPORAL CROSS CORRELATIONsipij
In this study we present a method of signal processing to determine dominant channels in near infrared spectroscopy (NIRS). To compare measuring channels and identify delays between them, cross correlation is computed. Furthermore, to find out possible dominant channels, a visual inspection was performed. The
outcomes demonstrated that the visual inspection exhibited evoked-related activations in the primary somatosensory cortex (S1) after stimulation which is consistent with comparable studies and the cross correlation study discovered dominant channels on both cerebral hemispheres. The analysis also showed a relationship between dominant channels and adjacent channels. For that reason, our results present a new
method to identify dominant regions in the cerebral cortex using near-infrared spectroscopy. These findings have also implications in the decrease of channels by eliminating irrelevant channels for the experiment.
Acquiring Practical Population Estimates of Neurons Through Design-Based Ster...EPL, Inc.
“Acquiring Practical Population Estimates of Neurons Through Design-Based Stereology: Dissecting the Disector.” Zadory D, Burton E, Wolf JC. The 44th Annual Meeting of the Society for Neuroscience. Washington, DC. November 19, 2014.
For full-resolution viewing, please open or save as a PDF.
Investigatng MultIfractality of Solar Irradiance Data Through Wavelet Based M...CSCJournals
It has been already revealed that the daily Solar Irradiance Data during the time period from October, 1984 to October, 2003 obtained by Earth Radiation Budget Satellite (ERBS) exhibits an Anti-persistent trend having multi-periodic phenomena. The solar irradiance time series data being a complex non linear signal in this paper we have tried to detect the irregularity and multifractality in the signal using continuous wavelet transform modulus maxima(WTMM) algorithm. Singularity spectrum of the signal has been obtained to measure the degree of multifractality of the Solar Irradiance signal.
PROFILE DOSE AND PDD ANALYSIS IN SMALL PHOTON FIELD WITH PTW PINPOINT CHAMBER...AM Publications
: Profile dose and PDD of a small field 6MV have been measured using a PTW PinPoint Chamber 0.015 cc detector. The aim of this study is to analyze the profile dose and PDD of small field using PTW PinPoint Chamber detector 0.015 cc. From the results of this study is expected optimal dose measurement accuracy can be achieved in clinical treatment. The analysis includes calculating the symmetry, penumbra value and PDD. Linear accelerator Electa Precise Treatment SystemTM and PTW PinPoint Chamber 0.015cc are the material used in this research. Profile Dose and PDD were measured with an SSD technique in 100 cm with a various of field sizes and depths. The results showed that the symmetry values for depth variations would decrease in ranges (0.55-6.27)% when the depth increase, and for field size variations will decrease in range (0.39-6.27)%. when the filed size increase. The result of the penumbra analysis shows that its value is more than 4 mm for all variations of depth and field size. PDD results showed that maximum dose for all fields size is achieved in maksimum depth (1.4 – 1.7) cm.
Similar to DevFest19 - Early Diagnosis of Chronic Diseases by Smartphone AI (20)
An introduction to Salesforce Commerce Cloud Development, this talk has been presented at Dreamforce, Melbourne Developer Group & Hyderabad Developer Groups in year 2018
Summer of Trailhead - Jaipur Developer user Group - Gaurav KheterpalGaurav Kheterpal
Slide deck introducing Summer of Trailhead and walking through key Trailhead concepts for Salesforce developers and admins.
Presented at 25th July, 2015
Dreamforce 2014 Mobile Theatre Session - Automated Testing for Salesforce1 Mo...Gaurav Kheterpal
My session at the Mobile Theatre in Dev Zone at Dreamforce 2014 on how to use the open source Appium framework for Salesforce1 mobile app automation testing
As part of global Salesforce1 Dev Week with over 70 meetups all over the world, I presented a session on 'Mobilise your apps with Salesforce1' - focusing on how you can leverage existing Apex/ VF skills to mobilise your app using Salesforce1
Salesforce Mobile DevWeek 21-28 April: Introduction to Native & Hybrid Develo...Gaurav Kheterpal
This session was meant to introduce the force.com Mobile SDK to all attendees of the Jaipur Developer User Group. This was a part of the global Salesforce Mobile Dev Week - http://www2.developerforce.com/mobile/developer-week
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Pushing the limits of ePRTC: 100ns holdover for 100 days
DevFest19 - Early Diagnosis of Chronic Diseases by Smartphone AI
1. Sabyasachi Mukhopadhyay
Kolkata Lead, Facebook Developer Circle
GDE in ML
Intel Software Innovator
Visiting Faculty, SCIT Pune
Co-Founder & Chief Research Officer, Twelit MedTech Pvt. Ltd
Early Diagnosis of Chronic Diseases by
Smartphone AI
2. What problem should we solve?
Use the process below to Identify, Articulate and
Probe new Problems worth solving
3. Goal: What is a problem worth solving?
Pick a theme e.g. ‘Digital Tech in Ag’
1. Resource Map
A. People and Orgs
B. Users and beneficiaries
C. Breakthroughs and risks
2. Problem Canvas
3. Solution Canvas
4. Findings Plot
4. 1. Resources Map
A. People and Orgs
B. Users and beneficiaries
C.Breakthroughs and risks
5. 1A. Orgs/Lists
• Orgs, Companies, Startups, Govt. dept
• Data sets and information sources
• Partners
• People, VCs, Influentials
• Known Published Challenges (e.g. UN, Gates, NSF, NIH,
DBT/DST)
6. 1B. Users and beneficiaries
• Graph of users and use cases
• Influencers
• Buyers/sponsors
• Partners
• Where do we need research?
• Speak with experts!
7. 1C. Breakthroughs, benefits and risks
• Breakthroughs, Parallel tech, Trends
• Payoffs, results, benefits IMPACT
• Friction – regulation, biases, etc.
• Financial risks
• Execution risks/ limiting factors (e.g., time, distance, lack
of existing infrastructure)
10. 4. Findings Plot
• Take a guess on two major factors that matter to you (usually cost and time).Cost is a proxy for
complexity etc. Time can be proxy for Engg time, regulation, market penetration etc.
11. MILESTONES
Map of users, resource requirements, dependencies, risks
and workarounds, steps, decision points
----------
13. Heilmeier questions*
1. What are you trying to do? Articulate objectives without using jargon.
2. How is it done today? What are the limits of current practice?
3. What’s new in your approach? Why do you think it will be successful?
4. Who cares?
5. If you’re successful, what difference will it make?
6. What are the risks and payoffs?
7. How much will it cost?
8. How long will it take?
9. What are the midterm and final “exams” to check for success?
* 9 questions to develop a meaningful project
16. Introduction
Optical Methods for tissue characterization
Light scattering
form Biological
tissue
Elastic Scattering
spectra from
biological tissue
Morphological information: Shape and size,
refractive index, intra-cellular organelles etc.
Micro-optical property: Fractal parameters,
disorder strength
Inelastic Scattering: Fluorescence and Raman Spectroscopy
Biochemical Information
17. Present interest: Light scattering based inverse analysis models – quantification of sub-
diffractional changes in tissue refractive index fluctuations.
Monofractal approximation may not be realistic in biological tissue having wide range of
dimensions and the complex nature of the spatial correlations
Spatial distribution of refractive index (RI) in biological
tissue observed to exhibit statistical self-similarity
Analyzed using fractal-Born approximation
--------- Phys. Rev. Lett. 97, 138102 (2006).
Multifractal?
Multiple scaling exponents Multi-resolution analysis
Scale invariant, power law dependence, single scaling
exponent
18. • Fractal / Self-similarity are process / geometry, which repeat its pattern in different scale of space
or time.
• Fractal dimension: measure the space filling capacity in geometry.
Fractals (self-similar) and Multifractals
( )P
2 EH D
Follows power law: ; being the spatial frequency. Fractal properties quantified by a single
scaling exponent, , where 𝑫 𝑬 = Euclidian Dimension, H = Hurst exponent; 0 < H <1.
Fractal dimension: 𝐷𝑓 = 𝐷 𝐸 − 𝐻 + 1.
19. • Observations-
• Power law scaling in wide range of spatial frequency with
background randomness-complexity of RI fluctuations
• Multiple scaling exponent is observed- Fourier analysis is
not adequate---
---- signature of multifractality?
DIC image: Connective Tissue
Unfolding the image
to 1D Spatial Refractive
Index (RI) variation
Fourier analysis
Observing multifractality in spatial refractive index fluctuations
Required a better multiresolution analysis to
characterize such Complex self-similar behavior.
Multifractal Detrended Fluctuation Analysis
(MFDFA) is one of the state of the art
multi-resolution analysis can be used.
20. Data
NixxiY
i
k
k ...,1,)(
1
)/int( sNNs
s
i
iyisY
s
sF
1
22
)(])1[(
1
),(
q
N
q
s
q
s
sF
N
sF
/1
2
1
2/2
),(
2
1
)(
)(
)( qh
q ssF
Multifractal Detrended Fluctuation Analysis (MFDFA)
Profile Y(i) Divide Y(i)
Local Fluctuation Fluctuation Function
Log-Log Plot of s vs. Fq(s) h(q)=
log[Fq(s)]
log(𝑠)
τ 𝑞 = 𝑞ℎ 𝑞 − 1 α = τ′ 𝑞 f(α) = qα - τ 𝑞
H. Eugene Stanley et al, Physica A 316 (2002) 87 – 114.
Width of the singularity spectrum (f(α)): 𝝈
N - length of the fluctuation
series
s - length of each segments
Order of moments q
)(sFq
Negative q capture
small fluctuations
Classical scaling exponent, 𝜏(q)
Hurst exponent, h(q)
21. Confirmation of multifractality: MFDFA analysis on DIC image
Multifractality confirmed:
---- Significant variation of slope with moment q - 𝒍𝒐𝒈[Fq(s)] vs. log(s)
22. Quantification of Multifractality in Different Grades of precancerous Tissue
Differences more prominent for negative q-values
h(q=2) at Higher grade
Width of singularity spectrum (σ) at Higher grade
Small scale (sub-m) fluctuations!
Stronger multifractality
Finer index fluctuations (roughness / heterogeneity)
23. Tissue Region Hurst exponent
( mean h(q=2) ± standard deviation)
Width of singularity spectra
(mean 𝛔 ± standard deviation)
Gr-I Gr-II Gr-III Gr-I Gr-II Gr-III
Connective Tissue 0.54 ± 0.03 0.50 ± 0.04 0.36 ± 0.08 0.60 ± 0.10 0.68 ± 0.13 0.88 ± 0.07
Spatial refractive index variation in biological tissue exhibits multifractality
direct evidence (MFDFA on DIC image)
Need of the hour: Extracting multifractal information from Light scattering
-------- clinically amenable and in-situ deployable
Key results
MFDFA results on different pathology grades
SPIE Proceedings, 2014
24. Scheme of extracting multifractality from light scattering spectra
Elastic scattering spectra from
tissue
Fourier domain inverse pre-
processing
Subjected to MFDFA
Strength of Multifractality = 𝛔;
Hurst exponent: h(q=2)
𝜼/
𝝆 : representative index
inhomogeneity distribution in
spatial scale ρ
25. Extraction of multifractality from tissue light scattering spectra
Scattering spectra from tissue
Experimental light scattering spectroscopic system
Multiple power law exponent
--- signature of multifractality?
Wave length variation [I()]; Fixed
26. Sample Preparation
Biopsied cervical precancer tissue slices (The histopathologically
characterized grade I, grade II, grade III precancer tissue) and normal tissues
were collected from Ganesh Shankar Vidyarthi Memorial (GSVM) Medical
College, Kanpur, India (age of patients between 35 – 60 years; ntotal = 35, with
ngrade I = 14, ngrade II = 6, ngrade III = 9; four biopsies from the normal
counterparts, nnormal = 6). The Standardized histological preparation of the
excised tissues involving fixation, dehydration, imbedding in wax, sectioning
under a rotary microtome with thickness ∼5 μm, lateral dimension ∼4 mm ×
6 mm, is followed by performing subsequent de-waxing. The consent for the
use of all the intact tissue (human cervix with cancer and normal) samples in
our study was obtained from the Ethical Committee, G. S. V. M.
Medical College and Hospital, Kanpur, India. The sample preparation methods
follow approved guidelines in our study.
27. Experimental System
The spatial distribution of tissue refractive index was recorded by a differential interference1
contrast (DIC) microscope (Olympus IX-81, USA). At a magnification of 60X, these DIC2
Images were recorded by a CCD camera (ORCA-ERG, Hamamatsu, 1344 X 1024 pixels pixel3
dimension 6.45 m). The elastic scattering spectra from the multiple sites of the biopsied tissue4
sections were recorded by the angle resolved spectral light scattering measurements (Figure 1).5
In brief, light emitted from a Xe-lamp (HPX-2000, Ocean Optics, USA) was collimated by a6
combination of lenses and illuminated the tissue sample at the centre of a goniometric7
arrangement (spot size ~1-mm-diameter). The collimated scattered light from sample was8
focused into a collecting fiber probe coupled to a spectrometer (USB4000FL, Ocean Optics,9
USA) for wavelength resolved signal detection. The recordings of spectra were performed (360-10
800nm) with a spectral resolution of 2.05 nm, where the angular range was kept at 10 150 11
with an interval of10. For the inverse multifractal study the spectra were recorded at12
backscattering angle = 150o
13
28. Schematic of the spectral light scattering measurement. Xe lamp: excitation source; A:
aperture; L1: collimating lens;L2: illuminating lens; L3 and L4: collecting lenses; f1: Focal
length of collimating lens L1; f2: focal length of illuminating lens L2 ; f3 and f4: Focal
lengths of collecting lenses L3 and L4 respectively.
29. Extraction and quantification of tissue multifractal parameters
Light scattering intensity
from Grade I tissue
Detrended refractive index
Fluctuations extracted from
Fourier domain preprocessed
signal
Multifractality
confirmed:-
Significant variation of
slopes with moment q-
𝒍𝒐𝒈[Fq(s)] vs. log(s)
Prominent variation in negative q-values Small scale (sub-m) fluctuations
30. Summary of results on tissue
Consistency of the multifractal trends
statistical significance of the observed differences in the multifractal parameters
No. of tissue samples studied: Healthy-20,Grade I -15, Grade II- 15 and Grade III -25
Hurst exponent, h(q=2) --- implies overall reduction in the depth correlation of refractive index
Strength of multifractality, σ -- indicates the increased of roughness / inhomogeneity of depth
resolved index variations
Possible reasons: fibrous network in connective tissue gets fragmented due to the shortening /
breaking of the building blocks, the collagen fibers / micro-fibril with the progression of cancer /
precancer.
J. Opt. 18(12), 125301 (2016)
Normal Grade-I Grade-II Grade-III
Hurst Exponent(h(q=2)) 0.630.02 0.560.05 0.480.03 0.410.04
Singularity Spectrum
Width (∆α)
0.860.01 0.900.03 0.960.04 0.990.01
31. Our initial dataset consists of 75 samples. We have used Monte Carlo cross-validation,
where we randomly split the dataset into training and testing dataset. This process has
been repeated 100 times. The size of training and testing dataset varies for each split;
we just ensured that a minimum (2) no of samples have been included in both training
and testing dataset for each split. For each such split, the model has been fit to the
training data, and predictive accuracy has been assessed using just the validation data
(testing data). Then the results of the testing data have been averaged over the splits.
The advantage of this method is that the proportion of the training/validation split is not
dependent on the number of iterations and the predictive accuracy is more or less
independent of the samples used for training dataset. We additionally have used 9
unknown samples taken at different time than the dataset and the prediction done by
our model has been compared by manual verification. Results of the unknown samples
also have been averaged and presented in the results. The above process has been
repeated for SVM and HMM.
32. Support vector machine (SVM) based multiclass classification on extracted
multifractal parameters from tissue samples.
33. SVM based tissue classification based on light scattering –derived multifractal tissue optical
properties. The horizontal surfaces of these figures have been sectioned into 4 4 rectangles.
The accurate and inaccurate prediction of each stages (normal, grade I, grade II, grade III)
have been represented by diagonal and off diagonal rectangles respectively.
Normal
Grade I
Grade II
Grade III
0
10
20
30
40
50
60
70
80
90
100
Normal
Grade I
Grade II
Grade III
98.5
14.29
10
0
1.5
57.14
35
0
0
28.57
55
0
0
0
0
100
Normal
Grade I
Grade II
Grade III
34. The flowchart of HMM based model on multifractal tissue optical properties
derived from light scattering spectra.
35. HMM based tissue classification based on light scattering –derived multifractal tissue optical
properties. The horizontal surfaces of these figures have been sectioned into 4 4 rectangles.
The accurate and inaccurate prediction of each stages (normal, grade I, grade II, grade III) have
been represented by diagonal and off diagonal rectangles respectively.
36. The results demonstrate that binary classification between normal and
cancerous tissues (grade III) are very good both in SVM and HMM. Meanwhile
in multiclass classification cases, when precancerous grades (grade I, grade II)
are to be classified along with normal and precancerous tissues (grade III),
abstract parameters achieved using HMM performs better than the SVM. The
presence of noise in the obtained signal damages the SVM performance as
SVM clearly classifies based on the kernel formed after considering all the
multifractal parameters. While in the case of HMM, the Markov model finds
abstract parameters by controlling the actual multifractal parameters and
produces prediction based on the derived abstract parameters. As a
consequence HMM avoids the noise added to the signal and able to produce
better multiclass classification results than SVM.
37. Probing depth resolved multifractality in human retina from Optical Coherence
Tomography (OCT) image
OCT can extract depth resolved index variation
Analyzed via MFDFA to extract alteration of multifractality due to progress of
diabetic macular edema (DME) and age related macular degeneracy.
• Cropped different layers of retina
Acquired in-vivo retinal OCT
Extracted depth resolved optical index variations of different layers
38. Variation of depth resolved optical index from OCT Images of in -vivo human retina
ILM: Inner limiting
membrane
NFL: Nerve fiber layer
GCL: Ganglion cell layer
IPL: Inner plexiform layer
INL: Inner nuclear layer
OPL: Outer plexiform layer
ONL: Outer nuclear layer
ELM: External limiting
membrane
IS/OS: Inner and outer
photoreceptor
RPE: Retinal pigment
epithelium
OPR: Outer segment
PR/RPE complex
Diabetic macular edema
39. Quantification of multifractality in depth resolved optical index variation in in-vivo
human retinal layers
Prominent difference in negative q indicates DME
related index alters mainly in small length scale
Analysis performed on Outer plexiform layer(OPL)
h(q=2) in OPL of diabetic macular edema (DME): indicates reduction of optical index
correlation in progress of DME
σ in OPL of diabetic macular edema (DME): indicates increase of strength of multifractality or
heterogeneity in layers with progress of DME
variation of 𝒍𝒐𝒈[Fq(l)] vs. log(l) slope
variation of h(q):
multifractality
40. Retinal Layers h(q=2) σ
Healthy AMD Healthy AMD
NFL 0.87 ± 0.04 0.66 ± 0.01 0.62 ± 0.08 0.93 ± 0.09
GCL 0.81 ± 0.03 0.61 ± 0.02 0.85 ± 0.02 1.57 ± 0.07
IPL 0.72 ± 0.02 0.66 ± 0.03 0.77 ± 0.05 1.23 ± 0.08
OPL 0.62 ± 0.01 0.53 ± 0.02 0.65 ± 0.09 1.00 ± 0.10
ONL 0.61 ± 0.03 0.51 ± 0.02 1.02 ± 0.07 1.31 ± 0.06
Choroid 0.76 ± 0.04 0.57 ± 0.03 0.82 ± 0.08 1.27 ± 0.12
Results of multifractality in healthy and age related macular degeneracy (AMD) in
in-vivo human retinal layers
Hurst exponent, h(q=2) --- implies overall reduction in the depth correlation of refractive index
Strength of multifractality,σ -- indicates the increased of roughness / inhomogeneity of depth
resolved index variations
J. Biomed. Opt. 21(9), 096004 (2016)
41. [1] N.Das, S.Mukhopadhyay, N.Ghosh, J.Chhablani, A.Richhariya, K.D.Rao, N. K.Sahoo, “Investigation of Alterations in multifractality
in Optical Coherence Tomographic Images of In Vivo Human Retina”; Journal Of Biomedical Optics 21(9), 096004 (Sep 09, 2016).
[2] S.Mukhopadhyay, N.Das, I.Kurmi, A.Pradhan, N.Ghosh, P.K.Panigrahi, “Tissue multifractality and hidden Markov model based
integrated framework for optimum precancer dectection”, Journal Of Biomedical Optics 22(10), 105005 (Oct 19, 2017).
[3] S.Mukhopadhyay et al., “Recurrence Quantifications as Potential Bio-markers for Diagnosis of Pre-Cancer”, SPIE Proceeding, SPIE
Photonics West, 2017, USA.
[4] S.Mukhopadhyay et al., “Optical Diagnosis of Cervical Cancer by Intrinsic Mode Functions”, SPIE Proceeding, SPIE Photonics
West, 2017, USA.
[5] S.Pratiher, S.Mukhopadhyay, R.Barman, S.Pratiher, A.Pradhan, N.Ghosh, P.K.Panigrahi, “Optical Diagnosis of Cervical Cancer by
Higher Order Spectra and Boosting”, SPIE Proceeding, SPIE Photonics West, 2017, USA.
[6] S.Mukhopadhyay et al., “Optical diagnosis of colon and cervical cancer by support vector machine”, SPIE Proceeding, SPIE
Photonics Europe, Belgium, 2016, Europe.
[7] S.Mukhopadhyay et al., “S-TRANSFORM BASED FLUCTUATION ANALYSIS- A METHOD FOR PRECANCER
DETECTION”, IEEE Conference Proceeding, Microcom-2016, India.
[8] S.Mukhopadhyay et al., “Wavelet and multi-fractal based analysis on DIC images in epithelium region to detect and diagnose the
cancer progress among different grades of tissues”, SPIE Proceeding, SPIE Photonics Europe, Belgium, 2014, Europe.
[9]S.Mukhopadhyay et al., “Pre-cancer Detection by Wavelet Transform and Multi-fractality in various grades of DIC Stromal
Images”, SPIE Proceeding, SPIE Photonics West, 2014, USA.
[10] S.Mukhopadhyay et.al., “Efficacy of hidden Markov model over support vector machine on multiclass classification of healthy and
cancerous cervical tissues”, SPIE Proceeding, SPIE Photonics West, 2018, USA.
[11]S.Mukhopadhyay et al., “A two-stage framework for DIC image denoising and Gabor based GLCM feature extraction for pre-cancer
diagnosis”, SPIE Proceeding, SPIE Photonics West, 2018, USA.
My Publications Related To Early Stage Disease Detection
42. 9
Impact on BOP Level
Pain free Early Stage Cancer Diagnosis and Low Cost Portable Device
First Solution
Generate report in a few minutes where biopsy examination takes several
days
Second Solution
Automatic Solutions and Highly Efficient
Third Solution
Stay Away From Tobacco : Be Safe in the Sun : Eat Healthy and Get Active
44. Unique Selling Proposition
100 Times Cheaper than Biopsy and
Accuracy >95%
Portable & Lightweight and 80 Times
Faster than Biopsy
Replacing Painful and Cost Effective
Biopsy
47. Competitive Landscape of Our Innovation Twelit
Low Cost
Accuracy >95%
Smart Device
Data Driven
Cloud Based
48. Media Reports on Published Research: New technique for early detection of human eye
diseases
Televisions:
[1]Zee News: http://zeenews.india.com/health/this-new-technique-can-detect-human-eye-
diseases-early-1962133
[2]Odisha TV: https://odishatv.in/technology/new-technique-for-early-detection-of-human-
eye-diseases-184520/
[3]News World India: https://newsworldindia.in/lifestyle/now-early-detection-of-human-
eye-diseases-is-possible-know-how/240282/
[4]NewsX: http://www.newsx.com/health-and-science/50839-new-technique-for-early-
detection-of-human-eye-diseases
Global News Site:
Yahoo News: https://in.news.yahoo.com/technique-early-detection-human-eye-diseases-
081403358.html
Science Magazine:
Nature India: http://www.natureasia.com/en/nindia/article/10.1038/nindia.2016.169
49. Media Reports on Published Research: New technique for early detection of human
eye diseases
Newspapers
[1]The Hindu: http://www.thehindu.com/sci-tech/science/Indian-
scientists%E2%80%99-novel-approach-to-diagnose-retinal-
diseases/article17004895.ece#comments
[2]Business Standard: http://www.business-standard.com/article/news-ians/new-
technique-for-early-detection-of-human-eye-diseases-116122700377_1.html
[3]The Indian Xpress: https://theindianxpress.com/20056/
[4]The Economic Times: http://economictimes.indiatimes.com/news/science/new-
technique-for-early-detection-of-human-eye-
diseases/articleshow/56198412.cms?from=mdr
50. 24
24. Cancer Research Success
Newspapers & Research Magazines:
[1]The Hindu:http://www.thehindu.com/sci-tech/science/diagnosing-early-stage-
cervical-cancer-using-artificial-intelligence/article22267117.ece
[2]Business Standard:http://www.business-standard.com/article/news-
ians/computer-based-optical-method-detects-early-stage-cervical-cancer-
117122800553_1.html
[3]The Indian Express: http://indianexpress.com/article/lifestyle/health/computer-
based-optical-method-detects-early-stage-cervical-cancer-5002420/
[4]Analytics India Magazine: https://analyticsindiamag.com/ai-cancer-detection-india-
light-scatter-algorithm/
[5] Nature India
:https://www.natureasia.com/en/nindia/article/10.1038/nindia.2017.148
Stay Away From Tobacco : Be Safe in the Sun : Eat Healthy and Get Active
AI based early stage cancer detection
52. 25
Award On AI Based Early Stage Disease Detection
My futuristic research on ‘AI Based Early Stage Disease
Detection’ was nominated among top 10 finalists in Global
IMT X-Prize international competition in USA. Dr.Barmak
Heshmat, Research Scientist, MIT was one of the organizers of
IMT X-Prize global competition. The panel comprised of
eminent scientists and artists from MIT, NASA, Marvel
Studios (Franchises of block buster Hollywood movies like
X-Men, Spider Men). The competition theme was to produce
futuristic ideas for 'AI (Artificial Intelligence) for Good'. I was
among top 10 finalists but ended as a runner up.
53. [1] We are currently expanding our investigations towards in-vivo deployment of
this integrated approach for precancer detection using tissue light scattering
spectra.
[2] Industrial implications are going on at LVPEI, Hyderabad for early stage DME
and diabetic retinopathy detection of eyes.
Ongoing and Future Studies