SAM-Sode: Towards Faithful Explanations for Tiny Bacteria Detection
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 eXplainabl
Record details
Published: 20 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare · Transparency
Retrieved: 14 July 2026
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ethics.ai (20 May 2026), “SAM-Sode: Towards Faithful Explanations for Tiny Bacteria Detection,” evidence record 3969, https://ethics.ai/record/3969 (originally published by arXiv).
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