用自然图像预训练模型微调，提升医学影像分类准确率。
Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models
- 用DINOv2、MAE等自然域大模型微调医学图像数据。
- 在少量标注数据下，AIMv2等模型表现优于其他模型。
- 适用于医疗影像分类研究者，尤其关注迁移学习应用。
基于大规模数据的础模型是可执行多种任务的大规模预训练模型，新方法不断带来性能提升。本研究考察了DINOv2、MAE、VMamba、CoCa、SAM2和AIMv2等最新前沿础模型在医学图像分类中的应用，涵盖乳房摄影（CBIS-DDSM）、皮肤病变（ISIC2019）、糖尿病视网膜病变（APTOS2019）和胸部X光（CHEXPERT）数据集。通过微调这些模型并评估其配置，分析其在医学领域的潜力。结果表明，这些先进模型显著提升了分类效果，在标注数据有限的情况下仍表现出鲁棒性能。其中，AIMv2、DINOv2和SAM2表现最优，证明自然域训练的进步对医学领域有积极影响，能有效改善分类结果。代码已公开：https://github.com/sajjad-sh33/Medical-Transfer-Learning。
原文摘要 · Abstract (English)
Using massive datasets, foundation models are large-scale, pre-trained models that perform a wide range of tasks. These models have shown consistently improved results with the introduction of new methods. It is crucial to analyze how these trends impact the medical field and determine whether these advancements can drive meaningful change. This study investigates the application of recent state-of-the-art foundation models, DINOv2, MAE, VMamba, CoCa, SAM2, and AIMv2, for medical image classification. We explore their effectiveness on datasets including CBIS-DDSM for mammography, ISIC2019 for skin lesions, APTOS2019 for diabetic retinopathy, and CHEXPERT for chest radiographs. By fine-tuning these models and evaluating their configurations, we aim to understand the potential of these advancements in medical image classification. The results indicate that these advanced models significantly enhance classification outcomes, demonstrating robust performance despite limited labeled data. Based on our results, AIMv2, DINOv2, and SAM2 models outperformed others, demonstrating that progress in natural domain training has positively impacted the medical domain and improved classification outcomes. Our code is publicly available at: https://github.com/sajjad-sh33/Medical-Transfer-Learning.
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