Are Natural Domain Foundation Models Useful for Medical Image Classification?
2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)(2023)
摘要
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 in particular,
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.
更多查看译文
关键词
Applications,Biomedical / healthcare / medicine,Algorithms,Datasets and evaluations,Algorithms,Machine learning architectures,formulations,and algorithms
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要