Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification

• URL: http://arxiv.org/abs/2408.14770v1
• Date: Tue, 27 Aug 2024 04:18:18 GMT
• Title: Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification
• Authors: Sirui Li, Li Lin, Yijin Huang, Pujin Cheng, Xiaoying Tang,
• Abstract summary: In medical contexts, the imbalanced data distribution in long-tailed datasets, due to scarce labels for rare diseases, greatly impairs the diagnostic accuracy of deep learning models. Recent multimodal text-image supervised foundation models offer new solutions to data scarcity through effective representation learning. We propose a novel Text-guided Foundation model Adaptation for Long-Tailed medical image classification (TFA-LT) Our method achieves an accuracy improvement of up to 27.1%, highlighting the substantial potential of foundation model adaptation in this area.
• Score: 4.6651139122498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In medical contexts, the imbalanced data distribution in long-tailed datasets, due to scarce labels for rare diseases, greatly impairs the diagnostic accuracy of deep learning models. Recent multimodal text-image supervised foundation models offer new solutions to data scarcity through effective representation learning. However, their limited medical-specific pretraining hinders their performance in medical image classification relative to natural images. To address this issue, we propose a novel Text-guided Foundation model Adaptation for Long-Tailed medical image classification (TFA-LT). We adopt a two-stage training strategy, integrating representations from the foundation model using just two linear adapters and a single ensembler for balanced outcomes. Experimental results on two long-tailed medical image datasets validate the simplicity, lightweight and efficiency of our approach: requiring only 6.1% GPU memory usage of the current best-performing algorithm, our method achieves an accuracy improvement of up to 27.1%, highlighting the substantial potential of foundation model adaptation in this area.

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