Alvin Ihsani
Nvidia
12 Papers
49 Citations
Alvin Ihsani is an academic researcher from Nvidia. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 4, co-authored 10 publications.
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Papers
Federated Learning for Breast Density Classification: A Real-World Implementation.
Holger R. Roth,Ken Chang,Praveer Singh,Nir Neumark,Wenqi Li,Vikash Gupta,Sharut Gupta,Liangqiong Qu,Alvin Ihsani,Bernardo Bizzo,Yuhong Wen,Varun Buch,Meesam Shah,Felipe Kitamura,Matheus Ribeiro Furtado de Mendonça,Vitor Lavor,Ahmed Harouni,Colin B. Compas,Jesse Tetreault,Prerna Dogra,Yan Cheng,Selnur Erdal,Richard D. White,Behrooz Hashemian,Thomas J. Schultz,Miao Zhang,Adam McCarthy,B. Min Yun,Elshaimaa Sharaf,Katharina Hoebel,Jay B. Patel,Bryan Chen,Sean Ko,Evan Leibovitz,Etta D. Pisano,Laura Coombs,Daguang Xu,Keith J. Dreyer,Ittai Dayan,Ram C. Naidu,Mona Flores,Daniel L. Rubin,Jayashree Kalpathy-Cramer +42 more
- 08 Oct 2020
TL;DR: This study investigates the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting and shows that despite substantial differences among the datasets from all sites and without centralizing data, it can successfully train AI models in federation.
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images
Andres Diaz-Pinto,Sachidanand Alle,Alvin Ihsani,Muhammad Hamza Asad,Vishwesh Nath,Fernando Perez-Garcia,Pritesh Mehta,Wenqi Li,Holger R. Roth,Tom Vercauteren,Daguang Xu,Prerna Dogra,Sebastien Ourselin,Andrew Feng,M. Jorge Cardoso +14 more
TL;DR: MONAI Label as mentioned in this paper is a free and open-source framework that facilitates the development of applications based on artificial intelligence (AI) models that aim at reducing the time required to annotate radiology datasets.
A User Interface for Optimizing Radiologist Engagement in Image Data Curation for Artificial Intelligence.
Mutlu Demirer,Sema Candemir,Matthew T. Bigelow,Sarah M. Yu,Vikash Gupta,Luciano M. Prevedello,Richard D. White,Joseph S. Yu,Rainer Grimmer,Michael Wels,Andreas Wimmer,Abdul H. Halabi,Alvin Ihsani,Thomas F. O'Donnell,Barbaros S. Erdal +14 more
- 27 Nov 2019
TL;DR: GUI-component compatibility with common image analysis tools facilitates radiologist engagement in image data curation, including image annotation, supporting AI application development and evolution for medical imaging.
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DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images
Andres Diaz-Pinto,Pritesh Mehta,Sachidanand Alle,Muhammad Hamza Asad,Richard Brown,Vishwesh Nath,Alvin Ihsani,Michela Antonelli,Daniel Palkovics,Csaba Pinter,Ron N. Alkalay,Steve Pieper,Holger R. Roth,Daguang Xu,Prerna Dogra,Tom Vercauteren,Andrew Feng,Abood Quraini,Sebastien Ourselin,M. Jorge Cardoso +19 more
- 18 May 2023
TL;DR: DeepEdit as mentioned in this paper combines the power of two methods: a non-interactive (i.e. automatic segmentation using nnU-Net, UNET or UNETR) and an interactive segmentation method (e.g. DeepGrow), into a single deep learning model.
Federated Learning for Breast Density Classification: A Real-World Implementation
Holger R. Roth,Ken Chang,Praveer Singh,Nir Neumark,Wenqi Li,Vikash Gupta,Sharut Gupta,Liangqiong Qu,Alvin Ihsani,Bernardo Bizzo,Yuhong Wen,Varun Buch,Meesam Shah,Felipe Kitamura,Matheus Ribeiro Furtado de Mendonça,Vitor Lavor,Ahmed Harouni,Colin B. Compas,Jesse Tetreault,Prerna Dogra,Yan Cheng,Selnur Erdal,Richard D. White,Behrooz Hashemian,Thomas J. Schultz,Miao Zhang,Adam McCarthy,B. Min Yun,Elshaimaa Sharaf,Katharina Hoebel,Jay B. Patel,Bryan Chen,Sean Ko,Evan Leibovitz,Etta D. Pisano,Laura Coombs,Daguang Xu,Keith J. Dreyer,Ittai Dayan,Ram C. Naidu,Mona Flores,Daniel L. Rubin,Jayashree Kalpathy-Cramer +42 more
TL;DR: This paper investigated the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting, and showed that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone.
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