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Andrew Janowczyk
Ajay Basavanhally
Anant Madabhushi

Showing results (1-10 of 430) with videos related to

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Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|July 5, 2016
Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathologyAndrew Janowczyk, Ajay Basavanhally, Anant Madabhushi
Plos One|May 21, 2015
Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancerAjay Basavanhally, Satish Viswanath, Anant Madabhushi
Journal of Pathology Informatics|August 27, 2016
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use casesAndrew Janowczyk, Anant Madabhushi
JCO Clinical Cancer Informatics|March 11, 2020
Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image AnalysisYijiang Chen, Andrew Janowczyk, Anant Madabhushi
Journal of Pathology Informatics|June 15, 2013
Quantifying local heterogeneity via morphologic scale: Distinguishing tumoral from stromal regionsAndrew Janowczyk, Sharat Chandran, Anant Madabhushi
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention|March 1, 2014
Variable importance in nonlinear kernels (VINK): classification of digitized histopathologyShoshana Ginsburg, Sahirzeeshan Ali, George Lee, et al.
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|February 22, 2011
Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal dataAnant Madabhushi, Shannon Agner, Ajay Basavanhally, et al.
Medical Image Analysis|May 17, 2011
A high-throughput active contour scheme for segmentation of histopathological imageryJun Xu, Andrew Janowczyk, Sharat Chandran, et al.
Computer Methods in Biomechanics and Biomedical Engineering. Imaging & Visualization|May 8, 2018
A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology imagesAndrew Janowczyk, Scott Doyle, Hannah Gilmore, et al.
The Lancet. Digital Health|June 15, 2026
Precision medicine's inevitable trajectory toward rare-disease-sized cohorts: implications for machine learning and deep learningAndrew Janowczyk, Doron Merkler, Olivier Michielin, et al.
Pageof 43

Showing results (1-10 of 430) with videos related to

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Pageof 43
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|July 5, 2016
Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathologyAndrew Janowczyk, Ajay Basavanhally, Anant Madabhushi
Plos One|May 21, 2015
Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancerAjay Basavanhally, Satish Viswanath, Anant Madabhushi
Journal of Pathology Informatics|August 27, 2016
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use casesAndrew Janowczyk, Anant Madabhushi
JCO Clinical Cancer Informatics|March 11, 2020
Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image AnalysisYijiang Chen, Andrew Janowczyk, Anant Madabhushi
Journal of Pathology Informatics|June 15, 2013
Quantifying local heterogeneity via morphologic scale: Distinguishing tumoral from stromal regionsAndrew Janowczyk, Sharat Chandran, Anant Madabhushi
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention|March 1, 2014
Variable importance in nonlinear kernels (VINK): classification of digitized histopathologyShoshana Ginsburg, Sahirzeeshan Ali, George Lee, et al.
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|February 22, 2011
Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal dataAnant Madabhushi, Shannon Agner, Ajay Basavanhally, et al.
Medical Image Analysis|May 17, 2011
A high-throughput active contour scheme for segmentation of histopathological imageryJun Xu, Andrew Janowczyk, Sharat Chandran, et al.
Computer Methods in Biomechanics and Biomedical Engineering. Imaging & Visualization|May 8, 2018
A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology imagesAndrew Janowczyk, Scott Doyle, Hannah Gilmore, et al.
The Lancet. Digital Health|June 15, 2026
Precision medicine's inevitable trajectory toward rare-disease-sized cohorts: implications for machine learning and deep learningAndrew Janowczyk, Doron Merkler, Olivier Michielin, et al.
Pageof 43