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MedFMC, our novel dataset of 22,349 images from diverse clinical tasks, aims to bolster the use of pre-trained foundation models in medical image classification—a sector facing data scarcity. The application of MedFMC underscores the potential of these models and highlights the effectiveness of various baseline methods in terms of accuracy and cost-efficiency.

Our method, Connect Image and Text Embeddings (CITE), enhances pathological image classification by integrating biomedical text knowledge. CITE proves its superiority, particularly in low-data scenarios, with the PatchGastric stomach tumor dataset. Therefore, CITE provides a novel approach to enhancing data-efficient pathological image analysis using in-domain text knowledge.

Our method, Diffusion-Driven Adaptation (DDA), adapts test inputs to enhance model accuracy on shifted data. Utilizing a generative diffusion model with image guidance and classifier self-ensembling, DDA surpasses traditional model adaptation approaches in handling various types of data corruption, diverse data quantities, and dependencies, as confirmed by the ImageNet-C benchmark tests.