Are Natural Domain Foundation Models Useful for Medical Image Classification?

• URL: http://arxiv.org/abs/2310.19522v2
• Date: Tue, 14 Nov 2023 12:21:41 GMT
• Title: Are Natural Domain Foundation Models Useful for Medical Image Classification?
• Authors: Joana Pal\'es Huix and Adithya Raju Ganeshan and Johan Fredin Haslum and Magnus S\"oderberg and Christos Matsoukas and Kevin Smith
• Abstract summary: We evaluate the performance of five foundation models across four well-established medical imaging datasets. DINOv2 consistently outperforms the standard practice of ImageNet pretraining. Other foundation models failed to consistently beat this established baseline indicating limitations in their transferability to medical image classification tasks.
• Score: 2.7652948339147807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The deep learning field is converging towards the use of general foundation models that can be easily adapted for diverse tasks. While this paradigm shift has become common practice within the field of natural language processing, progress has been slower in computer vision. In this paper we attempt to address this issue by investigating the transferability of various state-of-the-art foundation models to medical image classification tasks. Specifically, we evaluate the performance of five foundation models, namely SAM, SEEM, DINOv2, BLIP, and OpenCLIP across four well-established medical imaging datasets. We explore different training settings to fully harness the potential of these models. Our study shows mixed results. DINOv2 consistently outperforms the standard practice of ImageNet pretraining. However, other foundation models failed to consistently beat this established baseline indicating limitations in their transferability to medical image classification tasks.

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