SAM-Sode: Towards Faithful Explanations for Tiny Bacteria Detection

TL;DR

SAM-Sode is a novel XAI framework that enhances interpretability in tiny bacteria detection by producing spatially refined, morphology-aware explanations, addressing limitations of traditional methods in complex backgrounds.

Contribution

The paper introduces SAM-Sode, a geometry-aware, dual-constraint XAI framework that improves explanation clarity and morphological coherence in tiny object detection.

Findings

Effectively suppresses background redundancy.

Significantly improves decision transparency.

Achieves better morphological explanation coherence.

Abstract

Interpretability in object detection provides crucial confidence support for clinical auxiliary diagnosis. However, in tiny bacteria detection, traditional explanation methods often suffer from blurred foreground boundaries and diffuse feature attribution due to the extreme sparsity of target morphological features and severe interference from complex backgrounds. Such limitations hinder the provision of logically coherent morphological evidence. To bridge this gap, we propose a novel eXplainable AI (XAI) framework, SAM-Sode. The framework innovatively transforms initial feature attribution maps into geometry-aware prompts, leveraging the prior knowledge of the foundation model (SAM3) to achieve spatial refinement and morphological reconstruction of the explanatory mappings. Furthermore, we introduce a dual-constraint mechanism based on physical significance and geometric alignment to perform instance-level denoising, generating coherent explanations that better align with human expert intuition. Experimental results on our self-constructed bacteria dataset with complex circuit backgrounds (containing 2,524 images) and other public datasets demonstrate that the proposed method effectively suppresses background redundancy and significantly enhances the decision-making transparency of tiny object detection.