Explainable artificial intelligence (XAI) methods have been proposed to interpret model decisions in classification and, more recently, in semantic segmentation. However, instance-level XAI for semantic segmentation, namely explanations focused on a single object among multiple instances of the same class, remains largely unexplored. Such explanations are particularly important in multi-lesional diseases to understand what drives the detection and contouring of a specific lesion. We propose instance-level explanation maps for semantic segmentation by extending SmoothGrad and Grad-CAM++ to obtain quantitative instance saliency. These methods were applied to the segmentation of white matter lesions (WMLs), a magnetic resonance imaging biomarker in multiple sclerosis. We used 4023 FLAIR and MPRAGE MRI scans from 687 patients collected at the University Hospital of Basel, Switzerland, with WML masks annotated by four expert clinicians. Three deep learning architectures, a 3D U-Net, nnU-Net, and Swin UNETR, were trained and evaluated, achieving normalized Dice scores of 0.71, 0.78, and 0.80, respectively. Instance saliency maps showed that the models relied primarily on FLAIR rather than MPRAGE for WML segmentation, with positive saliency inside lesions and negative saliency in their immediate neighborhood, consistent with clinical practice. Peak saliency values differed significantly across correct and incorrect predictions, suggesting that quantitative instance saliency may help identify segmentation errors. In conclusion, we introduce two architecture-agnostic XAI methods that provide quantitative instance-level explanations for semantic segmentation and support clinically meaningful interpretation of model decisions.

翻译：可解释人工智能（XAI）方法已被提出用于解释分类模型决策，近年来更扩展至语义分割领域。然而，针对语义分割的实例级XAI——即专注于同类多个实例中单个对象的解释方法——仍鲜有研究。此类解释对于多病灶疾病尤为重要，可帮助理解特定病灶检测与轮廓勾画的决策依据。本研究通过扩展SmoothGrad和Grad-CAM++方法，提出生成语义分割的实例级解释图谱，以获取定量化的实例显著性。这些方法应用于白质病灶（WML）分割任务，该病灶是多发性硬化症的磁共振成像生物标志物。我们使用来自瑞士巴塞尔大学医院687名患者的4023次FLAIR与MPRAGE磁共振扫描数据，其WML掩模由四位临床专家标注。通过训练并评估三种深度学习架构（3D U-Net、nnU-Net及Swin UNETR），分别获得0.71、0.78和0.80的归一化Dice分数。实例显著性图谱显示，模型主要依赖FLAIR序列而非MPRAGE进行WML分割，病灶内部呈现正显著性而邻近区域呈现负显著性，这与临床实践一致。正确与错误预测间的峰值显著性存在显著差异，表明定量实例显著性可能有助于识别分割错误。本研究提出的两种架构无关XAI方法，可为语义分割提供定量化的实例级解释，并支持对模型决策进行具有临床意义的解读。