Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification
<|reference_start|>Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification: 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.<|reference_end|>
@article{li2024text-guided,
    title={Text-guided Foundation Model Adaptation for Long-Tailed Medical Image
  Classification},
    author={Sirui Li, Li Lin, Yijin Huang, Pujin Cheng, Xiaoying Tang},
    journal={arXiv preprint arXiv:2408.14770},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14770},
    primaryClass={cs.CV}
}