S
Sharmila Majumdar
Researcher at University of California, San Francisco
Publications - 505
Citations - 29773
Sharmila Majumdar is an academic researcher from University of California, San Francisco. The author has contributed to research in topics: Osteoarthritis & Cartilage. The author has an hindex of 88, co-authored 477 publications receiving 27074 citations. Previous affiliations of Sharmila Majumdar include University of California & Georgia Regents University.
Papers
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Journal ArticleDOI
On the association between deep learning assessed mri morphological phenotype and 4-years changes in cartilage thickness and t2 relaxation times
M. Heß,Nikan K. Namiri,Bruno Nunes Astuto,J.J. Lee,Thomas M. Link,Sharmila Majumdar,Valentina Pedoia +6 more
TL;DR: In this paper , a deep learning-generated ROAMES phenotyping of osteoarthritis (OA) has been demonstrated to predict incidence of structural and symptomatic OA as well as likelihood of total knee replacement (TKR) within 96 months.
Journal ArticleDOI
Augmenting Osteoporosis Imaging with Machine Learning.
TL;DR: In this paper, the authors discuss how recent advancements in image processing and machine learning (ML) are shaping a new and exciting era for the osteoporosis imaging field and discuss specific design choices, giving the reader pointers to possible solutions of regression, segmentation, and classification tasks as well as discussing common mistakes.
Posted Content
Leveraging wisdom of the crowds to improve consensus among radiologists by real time, blinded collaborations on a digital swarm platform
Rutwik Shah,Bruno Astuto,Tyler Gleason,Will Fletcher,Justin Banaga,Kevin Sweetwood,Allen Ye,Rina Patel,Kevin C. McGill,Thomas M. Link,Jason C. Crane,Valentina Pedoia,Sharmila Majumdar +12 more
TL;DR: In this paper, the authors explored a solution modeled on biological swarms of bees to overcome the dual problems of low consensus and inter-personal bias, which limit non-dominant participants from expressing true opinions.
Journal ArticleDOI
Technology and Tool Development for BACPAC: Qualitative and Quantitative Analysis of Accelerated Lumbar Spine MRI with Deep-Learning Based MR Image Reconstruction at 3T.
Misung Han,Emma Bahroos,Madeline Hess,Cynthia Chin,Kenneth T. Gao,David D. Shin,Javier Villanueva-Meyer,Thomas M. Link,Valentina Pedoia,Sharmila Majumdar +9 more
TL;DR: In this paper, the authors evaluated whether combining fast acquisitions with deep-learning reconstruction can provide diagnostically useful images and quantitative assessment comparable to standard-of-care acquisitions for lumbar spine MRI.
Posted Content
Utilizing a digital swarm intelligence platform to improve consensus among radiologists and exploring its applications
Rutwik Shah,Bruno Astuto,Tyler Gleason,Will Fletcher,Justin Banaga,Kevin Sweetwood,Allen Ye,Rina Patel,Kevin C. McGill,Thomas M. Link,Jason C. Crane,Valentina Pedoia,Sharmila Majumdar +12 more
TL;DR: In this paper, the authors explored a solution modeled on biological swarms of bees to overcome the dual problems of low consensus and inter-personal bias, which limit non-dominant participants from expressing true opinions.