提升微小细菌检测的可解释性，让模型推理更符合医生判断。
SAM-Sode: Towards Faithful Explanations for Tiny Bacteria Detection
- 将特征图转为几何感知提示，利用SAM先验精修解释区域。
- 双约束机制抑制背景噪声，使解释与真实形态更一致。
- 适用于医学影像中微小目标的可解释分析，适合临床辅助诊断。
可解释性在目标检测中为临床辅助诊断提供关键可信支持。然而，在微小细菌检测中，传统解释方法常因目标形态特征极端稀疏及复杂背景干扰，导致前景边界模糊、特征归属分散，难以提供逻辑连贯的形态学证据。为此，我们提出一种新型可解释AI框架SAM-Sode。该框架创新性地将初始特征归因图转化为几何感知提示，利用基础模型（SAM3）的先验知识实现解释映射的空间精细化与形态重建。同时引入基于物理意义与几何对齐的双约束机制，进行实例级去噪，生成更贴近人类专家直觉的一致性解释。在自建含复杂电路背景的细菌数据集（2,524张图像）及其他公开数据集上的实验表明，该方法有效抑制背景冗余，显著提升微小目标检测的决策透明度。
原文摘要 · Abstract (English)
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.
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