Beyond Detection: Comparative Explainability Study on Trypanosoma cruzi Using CAMs and DETR Attention

Chagas disease, caused by Trypanosoma cruzi, demands accurate and interpretable detection methods to support clinical decision-making. While deep learning models such asYOLOv8 and DINO-DETR perform well on microscopy images, their lack of interpretability hinders clinical adoption. We present the first comparative Explainability study of CNN- andtransformer-based object detectors for Trypanosoma cruzi detection. For YOLOv8, we benchmark ten Class Activation Mapping explainable AI (CAM-XAI) methods across multiple internallayers, evaluating interpretability using Intersection-over-Union (IoU) and Energy-Based Pointing Game (EBPG). For DINO-DETR, we introduce a query-specific attention visualizationmethod that maps decoder attention of a query to image space without back propagation. Our results reveal complementary behaviors: CAMs highlight broad parasite regions, while DETR attention targets fine-grained, discriminative features. We further demonstrate that existing localization metrics are inadequate for shared heatmaps in multi-object settings, underscoring the needfor new localization evaluation metrics in medical explainability