BenchMD: A Benchmark for Modality-Agnostic Learning on Medical Images and Sensors

Kathryn Wantlin, Chenwei Wu,Shih-Cheng Huang,Oishi Banerjee, Farah Dadabhoy, Veeral Vipin Mehta, Ryan Wonhee Han, Fang Cao, Raja R. Narayan,Errol Colak,Adewole Adamson,Laura Heacock,Geoffrey H. Tison,Alex Tamkin,Pranav Rajpurkar


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Medical data poses a daunting challenge for AI algorithms: it exists in many different modalities, experiences frequent distribution shifts, and suffers from a scarcity of examples and labels. Recent advances, including transformers and self-supervised learning, promise a more universal approach that can be applied flexibly across these diverse conditions. To measure and drive progress in this direction, we present BenchMD: a benchmark that tests how modality-agnostic methods, including architectures and training techniques (e.g. self-supervised learning, ImageNet pretraining), perform on a diverse array of clinically-relevant medical tasks. BenchMD combines 19 publicly available datasets for 7 medical modalities, including 1D sensor data, 2D images, and 3D volumetric scans. Our benchmark reflects real-world data constraints by evaluating methods across a range of dataset sizes, including challenging few-shot settings that incentivize the use of pretraining. Finally, we evaluate performance on out-of-distribution data collected at different hospitals than the training data, representing naturally-occurring distribution shifts that frequently degrade the performance of medical AI models. Our baseline results demonstrate that no modality-agnostic technique achieves strong performance across all modalities, leaving ample room for improvement on the benchmark. Code is released at .
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