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

• URL: http://arxiv.org/abs/2303.13567v2
• Date: Thu, 13 Apr 2023 21:28:21 GMT
• Title: AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT
• Authors: 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
• Abstract summary: AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. We show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets.
• Score: 3.0888799865182395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Such limitation stems from barriers to large-scale data share and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. In this study, we show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals across five continents that comprise >10,000 patients with >1 million images. We also propose an FL strategy that leverages synthetically generated data to overcome class and size imbalances. We also describe the sources of data heterogeneity in the context of FL, and show how even among the correctly labeled populations, disparities can arise due to these biases.

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