提出可解释的弱监督医学图像分析方法，精准定位关键病灶。
INSIGHT: Explainable Weakly-Supervised Medical Image Analysis
- 通过热图作为归纳偏置，端到端生成诊断相关区域
- 在CT和病理切片数据集上分类与分割性能达最新水平
- 适合需要可解释性的临床辅助诊断场景
由于体积大，体部扫描和全幻灯片病理图像（WSIs）通常通过提取局部区域嵌入，再由聚合器做出预测。然而现有方法依赖事后可视化技术（如Grad-CAM），常无法定位微小但具有临床意义的细节。为此，我们提出INSIGHT，一种新颖的弱监督聚合器，将热图生成作为归纳偏置。基于预训练特征图，INSIGHT采用小卷积核检测模块捕捉精细结构，结合感受野更广的上下文模块抑制局部误报。生成的内部热图突出诊断相关区域。在CT和WSI基准测试中，INSIGHT实现了最先进的分类效果和高弱标签语义分割性能。项目网站与代码见：https://zhangdylan83.github.io/ewsmia/
原文摘要 · Abstract (English)
Due to their large sizes, volumetric scans and whole-slide pathology images (WSIs) are often processed by extracting embeddings from local regions and then an aggregator makes predictions from this set. However, current methods require post-hoc visualization techniques (e.g., Grad-CAM) and often fail to localize small yet clinically crucial details. To address these limitations, we introduce INSIGHT, a novel weakly-supervised aggregator that integrates heatmap generation as an inductive bias. Starting from pre-trained feature maps, INSIGHT employs a detection module with small convolutional kernels to capture fine details and a context module with a broader receptive field to suppress local false positives. The resulting internal heatmap highlights diagnostically relevant regions. On CT and WSI benchmarks, INSIGHT achieves state-of-the-art classification results and high weakly-labeled semantic segmentation performance. Project website and code are available at: https://zhangdylan83.github.io/ewsmia/
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