无需标注即可实现医疗图像分割，还支持可解释性分析。
No Masks Needed: Explainable AI for Deriving Segmentation from Classification
- 用预训练模型微调，直接从分类任务生成分割图。
- 在CBIS-DDSM等数据集上分割精度显著提升。
- 引入可解释性分析，帮助医生理解分割依据。
医学图像分割对现代医疗和计算机辅助诊断至关重要。尽管计算机视觉领域已探索使用预训练模型进行无监督分割，但这些方法在医学影像中的应用效果不佳。本文提出一种新方法，针对医学图像微调预训练模型，实现高精度分割并具备完整处理流程。该方法结合可解释人工智能技术生成重要性评分，提升分割可靠性。相比传统方法在标准基准上表现良好但在医疗场景中失效的情况，本方法在CBIS-DDSM、NuInsSeg和Kvasir-SEG等数据集上均取得更优结果。
原文摘要 · Abstract (English)
Medical image segmentation is vital for modern healthcare and is a key element of computer-aided diagnosis. While recent advancements in computer vision have explored unsupervised segmentation using pre-trained models, these methods have not been translated well to the medical imaging domain. In this work, we introduce a novel approach that fine-tunes pre-trained models specifically for medical images, achieving accurate segmentation with extensive processing. Our method integrates Explainable AI to generate relevance scores, enhancing the segmentation process. Unlike traditional methods that excel in standard benchmarks but falter in medical applications, our approach achieves improved results on datasets like CBIS-DDSM, NuInsSeg and Kvasir-SEG.
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