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Foundation models, often pre-trained with large-scale data, have achievedparamount success in jump-starting various vision and language applications.Recent advances further enable adapting foundation models in downstream tasksefficiently using only a few training samples, e.g., in-context learning. Yet,the application of such learning paradigms in medical image analysis remainsscarce due to the shortage of publicly accessible data and benchmarks. In thispaper, we aim at approaches adapting the foundation models for medical imageclassification and present a novel dataset and benchmark for the evaluation,i.e., examining the overall performance of accommodating the large-scalefoundation models downstream on a set of diverse real-world clinical tasks. Wecollect five sets of medical imaging data from multiple institutes targeting avariety of real-world clinical tasks (22,349 images in total), i.e., thoracicdiseases screening in X-rays, pathological lesion tissue screening, lesiondetection in endoscopy images, neonatal jaundice evaluation, and diabeticretinopathy grading. Results of multiple baseline methods are demonstratedusing the proposed dataset from both accuracy and cost-effective perspectives.

MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification