About
Working on a Global initiative of Breast Cancer (BC) Pathology (Digital Pathology)…
Articles by Dr.Raj
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How does our immune system react to the coronavirus?
How does our immune system react to the coronavirus?
By Dr.Raj Subramanian, PhD, PMP
Activity
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"They tried hard to educate me but I responded only to physics and mathematics with any ease and moderate enthusiasm." Growing up on a farm with…
"They tried hard to educate me but I responded only to physics and mathematics with any ease and moderate enthusiasm." Growing up on a farm with…
Liked by Dr.Raj Subramanian, PhD, PMP
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When is the right time to exit a business and start something new? Exiting Suburban Diagnostics was one of the most important and difficult…
When is the right time to exit a business and start something new? Exiting Suburban Diagnostics was one of the most important and difficult…
Liked by Dr.Raj Subramanian, PhD, PMP
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We’re searching for innovative minds who thrive on solving complex challenges. If you’re ready to turn your ideas into game-changing solutions, we’d…
We’re searching for innovative minds who thrive on solving complex challenges. If you’re ready to turn your ideas into game-changing solutions, we’d…
Liked by Dr.Raj Subramanian, PhD, PMP
Experience
Education
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Emory University School of Medicine
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Activities and Societies: Post Doctoral Research in AI Driven Clinical Imaging Diagnostics for Brain Tumor
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Activities and Societies: Collaborated (as an independent expert - Research Lead and Database Architect) with two graduate research institutions, written & submitted Four academic research grants proposals worth US$3.2 Million to NIH (National Institutes of Health, USA) on Computational Biology, involving Cancer Gene Sequence Analysis, Pattern Mining for both Protein-Coding and Non_Coding_Transcription Factors/Variants for Human Cancer genes by using HMM methods, Heuristic approaches, under Cloud environment
4+ years of research experience in Genomic data science by developing data/pattern mining algorithms, machine learning algorithms using Java, R, Python and MySQL and applying Mathematical concepts (Enumerative Combinatorics Concepts, Set Theory)
Thesis entitled “Enhanced Algorithms for mining the Complete-Set of Frequent Contiguous Patterns (FCPs) in DNA Sequences”
•Research and developed enhanced algorithms for classical Market Basket analysis problem with DNA sequences, which…4+ years of research experience in Genomic data science by developing data/pattern mining algorithms, machine learning algorithms using Java, R, Python and MySQL and applying Mathematical concepts (Enumerative Combinatorics Concepts, Set Theory)
Thesis entitled “Enhanced Algorithms for mining the Complete-Set of Frequent Contiguous Patterns (FCPs) in DNA Sequences”
•Research and developed enhanced algorithms for classical Market Basket analysis problem with DNA sequences, which facilitate identification of Sequence Homology, Sequence Pattern Matching, Repetitive DNA sequences, Motif, Cis-Regulatory Modules (CRM), Genetic Mutations, Transcription Factors and DNA-Drug Design process
Manuscripts reviewer for “IEEE/ACM Transactions on Computational Biology and Bioinformatics” journal
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Activities and Societies: M.Phil dissertation focussed on Analysis of Data Stream Management system (DSMS) for Big data, especially for Quants/Quantitative Finance
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Activities and Societies: Few Computer Science Papers
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Activities and Societies: Few Post Graduate Papers
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Licenses & Certifications
Publications
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Yolov5 AI Deep Learning model driven Nuclear Pleomorphism Grading on Breast Cancer Pathology WSI for Nottingham Cancer Grading
International Journal on Recent and Innovation Trends in Computing and Communication
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Web based Mitosis detection on Breast Cancer Whole Slide Images using Faster R-CNN and YOLOv5
(IJACSA) International Journal of Advanced Computer Science and Applications
Histological grading quantifies the tumor architecture and the cytology deviation of breast cancer against normal tissue. Nottingham Grading System grades the breast cancer classification and allot tumor scores. Mitotic detection is one of the major component in the Nottingham Grading System. Using a conventional microscope is time-consuming, semi-quantitative and have limited histological parameters. Digital scanners scan the tissue slice into high-resolution virtual images called whole slide…
Histological grading quantifies the tumor architecture and the cytology deviation of breast cancer against normal tissue. Nottingham Grading System grades the breast cancer classification and allot tumor scores. Mitotic detection is one of the major component in the Nottingham Grading System. Using a conventional microscope is time-consuming, semi-quantitative and have limited histological parameters. Digital scanners scan the tissue slice into high-resolution virtual images called whole slide images. Deep learning models on whole slide images provide a fast and accurate quantitative diagnosis. This paper proposes two deep learning models namely Faster R-CNN and YOLOv5 to detect mitosis on WSI. The proposed Deep Learning models uses 56258 annotated tiles for training/testing and provide F1 score as 84%. The proposed model uses a web-based imaging analysis and diagnosis platform called CADD4MBC for image uploading, Annotation and visualization. This paper proposes an end-to-end web based Deep Learning detection for Breast Cancer Mitosis.
Other authorsSee publication -
Automatic carcinoma identification from breast epithelial tissue WSI through U-Net deep learning network
IP Journal of Diagnostic Pathology and Oncology
Epithelium tissue covers and lines all internal organs of the body. Breast cancer carcinomas arise from
the epithelial cells of the breast. The epithelial component lines tubules, ducts, etc. and segmenting out
this region from other tissues is important to detect breast cancer. This paper proposes application of deep learning technique U-Net, to segment epithelium tissue automatically from a whole slide image (WSI) image. It also implements U-Net, an image segmentation algorithm to…Epithelium tissue covers and lines all internal organs of the body. Breast cancer carcinomas arise from
the epithelial cells of the breast. The epithelial component lines tubules, ducts, etc. and segmenting out
this region from other tissues is important to detect breast cancer. This paper proposes application of deep learning technique U-Net, to segment epithelium tissue automatically from a whole slide image (WSI) image. It also implements U-Net, an image segmentation algorithm to automatically learn the features of epithlium components from the experts-annotated WSI image dataset and tested the results. Various image processing techniques such as thresholding are applied to improve the quality of dataset before and after the training of images by U-Net. The performance of the U-Net is measured by statistic parameters Sørensen–Dice coefficient and F1 score. The automatic system generated an epithelium segmentation of accuracy of 0.932. -
Automatic Classification of Sentinel Lymph Node (SLN) Metastases in Breast Carcinoma Whole Slide Image (WSI) through DenseNet Deep Learning Network
IP Journal of Diagnostic Pathology and Oncology
The evaluation of lymph nodes’ metastasis is an important component of Tumor, Node, Metastasis
(TNM) breast cancer staging system for better clinical management and treatment. Assessing lymph node metastasis through histologic examination is the most accurate method. This paper proposes significantly advanced and faster image classification Convolutional Neural Network (CNN) model called Densenet-161 for lymph node metastasis. This paper uses pre-processing technique called image…The evaluation of lymph nodes’ metastasis is an important component of Tumor, Node, Metastasis
(TNM) breast cancer staging system for better clinical management and treatment. Assessing lymph node metastasis through histologic examination is the most accurate method. This paper proposes significantly advanced and faster image classification Convolutional Neural Network (CNN) model called Densenet-161 for lymph node metastasis. This paper uses pre-processing technique called image thresholding to improve the contrast intensities of the SLN images, which improves the performance of DenseNet. The experimental PCam dataset contains 327,680 patches extracted from 400 Haemotoxylin and Eosin (H&E) stained WSIs of breast cancer with sentinel lymph node sections. The proposed system has generated 94% accuracy for lymph node metastasis classification -
An Ensemble-based Active Learning for Breast Cancer Classification
2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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DNA-Drug_FCP: An efficient computational method for DNA-Drug Design using frequently repeated patterns in a large Human Genome Database
Journal of Chemical and Pharmaceutical Sciences
DNA-Drug design using identified FCPs in Human Genome Database. Implemented using MongoDB and Java
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Frequent Contiguous Pattern (FCP) Mining in Genomic Sequence Analysis and Pattern Discovery
Journal of Chemical and Pharmaceutical Sciences
The identification of short length of FCPs of length between 6 to 18 is needed in the following genomic sequence analysis functions and genetic pattern discovery tools development. (1). Repetitive DNA in Human Genome (2). Cis-regulatory modules (CRM) (3). Sequence Motif (4).Transcription Factor (5).Promoter of gene (6).Single Nucleotide Polymorphism (SNP).
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Positional_nFCP: Positions based Big Data algorithm to Identify n_length Frequent Contiguous Patterns (FCP) in a Large Human Genome Sequence Database
International Journal of Applied Engineering and Research
Positions based Big Data algorithm to Identify n_length Frequent Contiguous Patterns (FCP) in a Large Human Genome Sequence Database, implemented using Java and MySQL
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Frequent Contiguous Pattern (FCP) Mining Algorithms for Biological Data Sequences
International Journal of Computer Applications (IJCA)
Transaction sequences in market-basket analysis have large set of alphabets with small length, whereas bio-sequences have small set of alphabets of long length with gap. There is the difference in pattern finding algorithms of these two sequences. The chances of repeatedly occurring small patterns are high in bio-sequences than in the transaction sequences. These repeatedly occurring small patterns are called as Frequent Contiguous Patterns (FCP). The challenging task in pattern finding of…
Transaction sequences in market-basket analysis have large set of alphabets with small length, whereas bio-sequences have small set of alphabets of long length with gap. There is the difference in pattern finding algorithms of these two sequences. The chances of repeatedly occurring small patterns are high in bio-sequences than in the transaction sequences. These repeatedly occurring small patterns are called as Frequent Contiguous Patterns (FCP). The challenging task in pattern finding of bio-sequences is to find FCP. FCP gives clues for genetic discovery, functional analysis and also helps to assemble a whole genome of species. Most of the existing FCP algorithms are all based on Apriori method. They require repeated scanning of the database and large number of intermediate tables to produce the results. So, these algorithms require large space and high computational time. In this paper, we are analyzing few of the currently available FCP algorithms with their advantages and disadvantages.
Other authorsSee publication -
HeurisFCP - A Heuristic approach to Identify Frequent Contiguous Patterns (FCP) in Sequence Database
IEEE International Conference on Radar, Communication and Computing (ICRCC), 2012
Print ISBN: 978-1-4673-2756-5, INSPEC Accession Number: 13291636,
Digital Object Identifier : 10.1109/ICRCC.2012.6450563
Identifying Frequent Contiguous Patterns (FCP) means to find the frequently repeated contiguous pattern in Sequence Database (SDB). This Algorithm helps in many SDB applications such as to extract Motif or Regulatory Regions in Genomic SDB, to find repeated requiring items in the Inventory SDB and to find stock trading patterns in Stock Trading SDB, etc.Other authorsSee publication -
Design and Analysis of Efficient Data Processing Paradigm for Quantitative Finance
International Conference on Recent Trends in Information Processing & Computing - ICIPC’12
Quantitative Financial Analysis field leverages computer technologies to build models from large amounts of data to extract insight. In today’s networked world, the amount of data available to build and refine models has been increasing exponentially. Often times the difference between seizing an opportunity and missing one is the latency involved in processing this data. The need to do complex processing with minimal latency over large volumes of data has led to the evolution of different data…
Quantitative Financial Analysis field leverages computer technologies to build models from large amounts of data to extract insight. In today’s networked world, the amount of data available to build and refine models has been increasing exponentially. Often times the difference between seizing an opportunity and missing one is the latency involved in processing this data. The need to do complex processing with minimal latency over large volumes of data has led to the evolution of different data processing paradigms. Increasingly there is a need to develop an event-oriented application development paradigm to decrease the latency in processing large volumes of data. Recent technological advances have pushed the emergence of a new class of data-intensive applications that require continuous processing over sequences of transient data, called data streams, in near real-time.
This work presents a solid and powerful foundation for processing continuous queries over data streams. We (i) define a sound semantics for continuous sliding window queries, (ii) introduce a unique stream algebra implemented with efficient online algorithms, and (iii) sketch the adaptive runtime environment. Our work carries over and enhances findings from temporal databases to meet the challenging requirements of the data stream in Quantitative Financial Analysis Field. This will aid “Quants – Quantitative Financial Analysts” to develop Ideal solutions for High Data-intensive, Real-Time and Continuous Quantitative Finance Problems using Highly Efficient Data Processing Paradigm.Other authors -
SequentialPR_FCP: A BigData Pattern decomposing algorithm to Identify Frequent Contiguous Patterns (FCP) from Large DNA Sequence Database using Permutations with Repetition (PR)
International Journal of Applied Engineering and Research
Patents
Honors & Awards
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Tubules Detection on Breast Carcinoma Whole Slide Images using Artificial Intelligence Deep Learning Model
SIIM CMIM22 – Conference on Machine Intelligence in Medical Imaging
Presented at John Hopkins University, Baltimore, USA
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Progressional Analysis of Post-Covid Lung Fibrosis through AI-Deep Learning, AI-Driven Severity Scoring of % of Lung Involvement
European Society for Medicine - General Assembly, Madrid Spain
Invited Presentation
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BIG Grant - AI Driven Breast Cancer CADS (Computer Aided Diagnostic System) for Clinical Diagnostic Imaging Biomarkers
BIRAC - BioTechnology Industry Research Assistance Council, DBT, Govt. of India
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Deliver-IT Award
Microsoft CIO, Redmond, USA
US$5 Million budgeted project on refactoring and re-architecture of large MS-SQL Server Data Warehouse (2.5 TB) of critical Microsoft License Information which enabled additional revenue recognition of $120 Million/per day at Microsoft
Languages
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English
Full professional proficiency
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Tamil
Native or bilingual proficiency
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Hindi
Professional working proficiency
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Next-generation lung cancer🫁 pathology🔬: Development and validation of diagnostic and prognostic algorithms out on Cell Study presents a…
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CIIC had the privilege of welcoming His Excellency Dato Alihuddin Mohamad Taha, High Commissioner of Brunei Darussalam https://lnkd.in/g9rJ8gtk to…
CIIC had the privilege of welcoming His Excellency Dato Alihuddin Mohamad Taha, High Commissioner of Brunei Darussalam https://lnkd.in/g9rJ8gtk to…
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🚨 I will continue to emphasize the importance of clinical domain expertise in medical imaging AI research in the hopes of improving future research…
🚨 I will continue to emphasize the importance of clinical domain expertise in medical imaging AI research in the hopes of improving future research…
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Thrilled to report new USPTO patent 12,073,560 "Computational Features of Tumor-Infiltrating Lymphocyte (TIL) Architecture”, application of #AI to…
Thrilled to report new USPTO patent 12,073,560 "Computational Features of Tumor-Infiltrating Lymphocyte (TIL) Architecture”, application of #AI to…
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Struggling to stay on top of the digital pathology and AI developments? I used to get overwhelmed with everything being published… so I set up with…
Struggling to stay on top of the digital pathology and AI developments? I used to get overwhelmed with everything being published… so I set up with…
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Attention dear corporates, Seeking equity funding for Indian and international companies with a turnover of 100 crores and above? Our UK-based…
Attention dear corporates, Seeking equity funding for Indian and international companies with a turnover of 100 crores and above? Our UK-based…
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A recent study conducted by the Garvan Institute of Medical Research suggests that non-coding DNA, which comprises 98% of our genome and lacks the…
A recent study conducted by the Garvan Institute of Medical Research suggests that non-coding DNA, which comprises 98% of our genome and lacks the…
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“If you’re founding a HealthTech startup, you must be a little crazy—and Health Founders is where crazy people help other crazy people.” With these…
“If you’re founding a HealthTech startup, you must be a little crazy—and Health Founders is where crazy people help other crazy people.” With these…
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Global capability centres (GCCs) are quietly transforming the pharmaceutical and life sciences (PLS) sector with the innovative use of #GenAI. By…
Global capability centres (GCCs) are quietly transforming the pharmaceutical and life sciences (PLS) sector with the innovative use of #GenAI. By…
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