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

NanoBatch Privacy: Enabling fast Differentially Private learning on the IPU

no code implementations • 24 Sep 2021 • Edward H. Lee, Mario Michael Krell, Alexander Tsyplikhin, Victoria Rege, Errol Colak, Kristen W. Yeom

We also provide two extensions: 1) DPSGD for pipelined models and 2) per-layer clipping that is 15x faster than the Opacus implementation on 8x A100s.

Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction

no code implementations • 15 May 2019 • Hongjiang Wei, Steven Cao, Yuyao Zhang, Xiaojun Guan, Fuhua Yan, Kristen W. Yeom, Chunlei Liu

To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM.

Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet

1 code implementation • Medicine 2018 • Nicholas Bien, Pranav Rajpurkar, Robyn L. Ball, Jeremy Irvin, Allison Park, Erik Jones, Michael Bereket, Bhavik N. Patel, Kristen W. Yeom, Katie Shpanskaya, Safwan Halabi, Evan Zucker, Gary Fanton, Derek F. Amanatullah, Christopher F. Beaulieu, Geoffrey M. Riley, Russell J. Stewart, Francis G. Blankenberg, David B. Larson, Ricky H. Jones, Curtis P. Langlotz, Andrew Y. Ng, Matthew P. Lungren

Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries.

Deep Learning Multi-Label Classification