RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models

no code implementations • 16 Jan 2024 • Farhad Maleki, Linda Moy, Reza Forghani, Tapotosh Ghosh, Katie Ovens, Steve Langer, Pouria Rouzrokh, Bardia Khosravi, Ali Ganjizadeh, Daniel Warren, Roxana Daneshjou, Mana Moassefi, Atlas Haddadi Avval, Susan Sotardi, Neil Tenenholtz, Felipe Kitamura, Timothy Kline

Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of interest within medical images is crucial but manually laborious and prone to interobserver and intraobserver biases.

Image Segmentation Medical Image Segmentation +3

AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT

no code implementations • 23 Mar 2023 • Edward H. Lee, Brendan Kelly, Emre Altinmakas, Hakan Dogan, Maryam Mohammadzadeh, Errol Colak, Steve Fu, Olivia Choudhury, Ujjwal Ratan, Felipe Kitamura, Hernan Chaves, Jimmy Zheng, Mourad Said, Eduardo Reis, Jaekwang Lim, Patricia Yokoo, Courtney Mitchell, Golnaz Houshmand, Marzyeh Ghassemi, Ronan Killeen, Wendy Qiu, Joel Hayden, Farnaz Rafiee, Chad Klochko, Nicholas Bevins, Faeze Sazgara, S. Simon Wong, Michael Moseley, Safwan Halabi, Kristen W. Yeom

While it is well known that population differences from genetics, sex, race, and environmental factors contribute to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources.

Federated Learning

Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification

no code implementations • 3 Feb 2022 • Timothy L. Kline, Felipe Kitamura, Ian Pan, Amine M. Korchi, Neil Tenenholtz, Linda Moy, Judy Wawira Gichoya, Igor Santos, Steven Blumer, Misha Ysabel Hwang, Kim-Ann Git, Abishek Shroff, Elad Walach, George Shih, Steve Langer

The goal of this series is to provide resources to not only help improve the review process for A. I.-based medical imaging papers, but to facilitate a standard for the information that is presented within all components of the research study.

Image Classification

Federated Learning for Breast Density Classification: A Real-World Implementation

no code implementations • 3 Sep 2020 • Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu, Alvin Ihsani, Bernardo C. Bizzo, Yuhong Wen, Varun Buch, Meesam Shah, Felipe Kitamura, Matheus Mendonça, Vitor Lavor, Ahmed Harouni, Colin Compas, Jesse Tetreault, Prerna Dogra, Yan Cheng, Selnur Erdal, Richard White, Behrooz Hashemian, Thomas Schultz, Miao Zhang, Adam McCarthy, B. Min Yun, Elshaimaa Sharaf, Katharina V. 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 Rubin, Jayashree Kalpathy-Cramer

Building robust deep learning-based models requires large quantities of diverse training data.

breast density classification Classification +2