Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913481781936128 |
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| author | Li, Sirui Lin, Li Huang, Yijin Cheng, Pujin Tang, Xiaoying |
| author_facet | Li, Sirui Lin, Li Huang, Yijin Cheng, Pujin Tang, Xiaoying |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14770 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification Li, Sirui Lin, Li Huang, Yijin Cheng, Pujin Tang, Xiaoying Computer Vision and Pattern Recognition 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. |
| title | Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.14770 |