提出CIG方法，让病理图像分类更可解释，精准定位肿瘤区域
Contrastive Integrated Gradients: A Feature Attribution-Based Method for Explaining Whole Slide Image Classification
- 通过对比不同类别梯度，突出区分肿瘤与正常组织的关键区域
- 在三个癌症数据集上，解释力比传统方法提升，且符合归因理论要求
- 适合需要可信AI诊断的病理分析场景，尤其弱监督条件下表现优
可解释性对计算病理学中的全切片图像（WSI）分析至关重要，有助于建立对AI辅助诊断的信任。尽管积分梯度（IG）等归因方法已有潜力，但直接应用于高分辨率WSI时面临挑战，易忽略区分肿瘤亚型的关键信号。本文提出对比积分梯度（CIG），在logit空间计算对比梯度，首先通过相对于参考类别的特征重要性比较，更清晰地区分肿瘤与非肿瘤区域；其次满足积分归因的公理，保证理论一致性；第三，提出两个质量评估指标MIL-AIC和MIL-SIC，衡量在逐步引入显著区域时预测信息与模型置信度的变化，尤其适用于弱监督场景。在涵盖三种癌症类型的三个数据集（CAMELYON16、TCGA-RCC、TCGA-Lung）上验证，结果表明CIG在定量（基于MIL-AIC和MIL-SIC）和定性（可视化与真实肿瘤区域高度吻合）上均优于现有方法，展现出在可解释、可信的WSI诊断中的潜力。
原文摘要 · Abstract (English)
Interpretability is essential in Whole Slide Image (WSI) analysis for computational pathology, where understanding model predictions helps build trust in AI-assisted diagnostics. While Integrated Gradients (IG) and related attribution methods have shown promise, applying them directly to WSIs introduces challenges due to their high-resolution nature. These methods capture model decision patterns but may overlook class-discriminative signals that are crucial for distinguishing between tumor subtypes. In this work, we introduce Contrastive Integrated Gradients (CIG), a novel attribution method that enhances interpretability by computing contrastive gradients in logit space. First, CIG highlights class-discriminative regions by comparing feature importance relative to a reference class, offering sharper differentiation between tumor and non-tumor areas. Second, CIG satisfies the axioms of integrated attribution, ensuring consistency and theoretical soundness. Third, we propose two attribution quality metrics, MIL-AIC and MIL-SIC, which measure how predictive information and model confidence evolve with access to salient regions, particularly under weak supervision. We validate CIG across three datasets spanning distinct cancer types: CAMELYON16 (breast cancer metastasis in lymph nodes), TCGA-RCC (renal cell carcinoma), and TCGA-Lung (lung cancer). Experimental results demonstrate that CIG yields more informative attributions both quantitatively, using MIL-AIC and MIL-SIC, and qualitatively, through visualizations that align closely with ground truth tumor regions, underscoring its potential for interpretable and trustworthy WSI-based diagnostics
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