用局部提示+最优传输提升CLIP在医学异常检测中的表现
MADPOT: Medical Anomaly Detection with CLIP Adaptation and Partial Optimal Transport
- 多提示结合最优传输，捕捉局部异常特征
- 少样本、零样本和跨数据集均达顶尖性能
- 无需合成数据或记忆库，适合医疗图像分析
医学异常检测因成像模态多样、解剖结构差异大及标注数据有限而极具挑战。本文提出MADPOT方法，通过视觉适配器与提示学习结合部分最优传输（POT）和对比学习（CL），增强CLIP对医学图像的适应性，尤其适用于异常检测。与传统提示学习仅生成单一表征不同，本方法利用POT对齐多个提示与局部特征，更精准捕获细微异常；对比学习进一步强化类内紧凑性和类间分离性。实验表明，该方法在少样本、零样本及跨数据集场景下均达到领先水平，且无需合成数据或记忆库。代码已开源：https://github.com/mahshid1998/MADPOT。
原文摘要 · Abstract (English)
Medical anomaly detection (AD) is challenging due to diverse imaging modalities, anatomical variations, and limited labeled data. We propose a novel approach combining visual adapters and prompt learning with Partial Optimal Transport (POT) and contrastive learning (CL) to improve CLIP's adaptability to medical images, particularly for AD. Unlike standard prompt learning, which often yields a single representation, our method employs multiple prompts aligned with local features via POT to capture subtle abnormalities. CL further enforces intra-class cohesion and inter-class separation. Our method achieves state-of-the-art results in few-shot, zero-shot, and cross-dataset scenarios without synthetic data or memory banks. The code is available at https://github.com/mahshid1998/MADPOT.
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