Federated learning improves site performance in multicenter deep learning without data sharing

Autor: Leonard S. Marks, William Speier, Baris Turkbey, Bradford J. Wood, Steven S. Raman, Thomas Sanford, Rushikesh Kulkarni, Alex G. Raman, Jesse Tetreault, Alan Priester, Mona Flores, Holger R. Roth, Daguang Xu, Ziyue Xu, Peter L. Choyke, Corey W. Arnold, Karthik V. Sarma, Stephanie Harmon, Dieter R. Enzmann

Publikováno v: Journal of the American Medical Informatics Association : JAMIA, vol 28, iss 6
Journal of the American Medical Informatics Association : JAMIA

Objective To demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL). Materials and Methods Deep learning models were trained at each participating institution using l

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cfcc43846600977c2b828330a2b4c1eb
https://doi.org/10.1093/jamia/ocaa341

Harnessing clinical annotations to improve deep learning performance in prostate segmentation

Autor: William Speier, Leonard S. Marks, Stephanie Harmon, Steven S. Raman, Alex G. Raman, Karthik V. Sarma, Baris Turkbey, Thomas Sanford, Alan Priester, Corey W. Arnold, Nikhil J. Dhinagar, Sherif Mehralivand

Publikováno v: PLoS ONE, Vol 16, Iss 6, p e0253829 (2021)
PLoS ONE

Purpose Developing large-scale datasets with research-quality annotations is challenging due to the high cost of refining clinically generated markup into high precision annotations. We evaluated the direct use of a large dataset with only clinically

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9d2e2f923d1ae660e53e8503da596f14
https://doaj.org/article/614ca6a1ac7743129fe5e2c5dec4ac65

MP78-04 PREDICTING PROSTATE CANCER FOCAL THERAPY ELIGIBILITY WITH MACHINE LEARNING

Autor: Steve Zhou, Leonard S. Marks, Shyam Natarajan, Jorge Ballon, David W. Johnson, Alex G. Raman, Karthik V. Sarma, Jason J. Yang, Alan Priester, Rajiv Jayadevan, Corey W. Arnold

Publikováno v: Journal of Urology. 201

INTRODUCTION AND OBJECTIVES:Focal therapies such as hemi-gland ablation (HGA) are evolving treatment options for prostate cancer. However, limitations in current diagnostics--biopsy Gleason score, ...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f938040d31e59074df8f4af26591c2bf
https://doi.org/10.1097/01.ju.0000557336.01833.27

Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images

Autor: King Chung Ho, Jiayun Li, Shiwen Shen, Corey W. Arnold, Arkadiusz Gertych, Wenyuan Li, Beatrice S. Knudsen, Karthik V. Sarma

Publikováno v: IEEE transactions on medical imaging, vol 38, iss 4

Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4cfbc2e3f29dc51687038cec3a8b02f
https://europepmc.org/articles/PMC6497079/

An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies

Autor: Arkadiusz Gertych, William Speier, Jiayun Li, Corey W. Arnold, King Chung Ho, Beatrice S. Knudsen, Karthik V. Sarma

Publikováno v: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. 69

Automated Gleason grading is an important preliminary step for quantitative histopathological feature extraction. Different from the traditional task of classifying small pre-selected homogeneous regions, semantic segmentation provides pixel-wise Gle

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68a0188458b8a0adc0fdd97746cf0b72
https://pubmed.ncbi.nlm.nih.gov/30243216