Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Niranjan Balachandar"'
Autor:
Vignesh Kumaresan, Niranjan Balachandar, Sarah F Poole, Lance J Myers, Paul Varghese, Vindell Washington, Yugang Jia, Vivian S Lee
Publikováno v:
PLoS ONE, Vol 18, Iss 3, p e0283517 (2023)
COVID-19 forecasting models have been critical in guiding decision-making on surveillance testing, social distancing, and vaccination requirements. Beyond influencing public health policies, an accurate COVID-19 forecasting model can impact community
Externí odkaz:
https://doaj.org/article/2e0c0d024d70452a9d69e9573982881a
Autor:
Niranjan Balachandar, Chris Piech, Namperumalsamy Venkatesh Prajna, Prajna Lalitha, Thomas M. Lietman, Janice T. Chua, Travis Redd, Muthiah Srinivasan, Charles P. Lin, Medina Baitemirova, Natacha C Villegas, Mo Tiwari, Sebastian Thrun
Publikováno v:
Ophthalmology
Ophthalmology, vol 129, iss 2
Ophthalmology, vol 129, iss 2
Purpose To develop and evaluate an automated, portable algorithm to differentiate active corneal ulcers from healed scars using only external photographs. Design A convolutional neural network was trained and tested using photographs of corneal ulcer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::785e51c44d266312ac79b3c0813a7154
https://europepmc.org/articles/PMC8792172/
https://europepmc.org/articles/PMC8792172/
Autor:
Zhiyong Lu, Sungwon Lee, Yifan Peng, Thomas C. Shen, Niranjan Balachandar, Angshuman Paul, Ronald M. Summers
Publikováno v:
IEEE Trans Med Imaging
Zero-shot learning (ZSL) is one of the most promising avenues of annotation-efficient machine learning. In the era of deep learning, ZSL techniques have achieved unprecedented success. However, the developments of ZSL methods have taken place mostly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::abf7d3c4b724a98663caf9e7e8f324ad
https://pubmed.ncbi.nlm.nih.gov/33523805
https://pubmed.ncbi.nlm.nih.gov/33523805
Publikováno v:
Med Image Anal
Collaborative learning, which enables collaborative and decentralized training of deep neural networks at multiple institutions in a privacy-preserving manner, is rapidly emerging as a valuable technique in healthcare applications. However, its distr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74aaaefbfcccf36a9affa5236628165f
https://doi.org/10.1016/j.media.2022.102424
https://doi.org/10.1016/j.media.2022.102424
Accounting for data variability in multi-institutional distributed deep learning for medical imaging
Publikováno v:
J Am Med Inform Assoc
Objectives Sharing patient data across institutions to train generalizable deep learning models is challenging due to regulatory and technical hurdles. Distributed learning, where model weights are shared instead of patient data, presents an attracti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c114d92cabb596c608af99c0bf5c09ff
https://europepmc.org/articles/PMC7309257/
https://europepmc.org/articles/PMC7309257/
Autor:
Thomas C. Shen, Zhiyong Lu, Yuxing Tang, Niranjan Balachandar, Angshuman Paul, Yifan Peng, Ronald M. Summers
Publikováno v:
Interpretable and Annotation-Efficient Learning for Medical Image Computing ISBN: 9783030611651
iMIMIC/MIL3iD/LABELS@MICCAI
iMIMIC/MIL3iD/LABELS@MICCAI
Zero-shot learning, in spite of its recent popularity, remains an unexplored area for medical image analysis. We introduce a first-of-its-kind generalized zero-shot learning (GZSL) framework that utilizes information from two different imaging modali
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::36c83db064224af1b0f661de448bed60
https://doi.org/10.1007/978-3-030-61166-8_11
https://doi.org/10.1007/978-3-030-61166-8_11
Autor:
Jayashree Kalpathy-Cramer, Daniel L. Rubin, Carson Lam, Bruce R. Rosen, James M. Brown, Darvin Yi, Andrew Beers, Niranjan Balachandar, Ken Chang
Publikováno v:
Journal of the American Medical Informatics Association : JAMIA
Objective Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::61fecb0035505cbb9ce337f4f84b637f
https://doi.org/10.1093/jamia/ocy017
https://doi.org/10.1093/jamia/ocy017
Publikováno v:
Journal of the American Medical Informatics Association. 27:1340-1340
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ebb0aca7d158da87488ecc8413d4106
https://doi.org/10.1093/jamia/ocaa118
https://doi.org/10.1093/jamia/ocaa118