用少量标注引导模型聚焦病灶区域，提升医学影像诊断的可解释性。
GFSR-Net: Guided Focus via Segment-Wise Relevance Network for Interpretable Deep Learning in Medical Imaging
- 通过局部标注引导模型关注病灶区域，无需精确边界。
- 在胸部X光、眼底图等数据上实现高准确率与符合人眼预期的热力图。
- 适合需要可解释性的临床辅助诊断场景，提升医生信任度。
深度学习在医学图像分析中取得显著进展，但其临床应用受限于缺乏可解释性。现有模型虽能正确分类，却无法说明推理依据，甚至依赖无关区域或标注信息，导致诊断不可靠。本文提出导向聚焦的分段相关性网络（GFSR-Net），利用少量人工标注近似人类关注区域，无需精确边界或全面标记，使标注过程快速实用。训练过程中，模型学习对诊断相关特征进行逐步强化，实现注意力对齐。该方法适用于自然图像与多种医学图像，包括胸部X光、视网膜扫描和皮肤图像。实验表明，GFSR-Net在保持或超越原有精度的同时，生成更符合人类预期的显著性图，减少对无关模式的依赖，增强自动化诊断工具的可信度。
原文摘要 · Abstract (English)
Deep learning has achieved remarkable success in medical image analysis, however its adoption in clinical practice is limited by a lack of interpretability. These models often make correct predictions without explaining their reasoning. They may also rely on image regions unrelated to the disease or visual cues, such as annotations, that are not present in real-world conditions. This can reduce trust and increase the risk of misleading diagnoses. We introduce the Guided Focus via Segment-Wise Relevance Network (GFSR-Net), an approach designed to improve interpretability and reliability in medical imaging. GFSR-Net uses a small number of human annotations to approximate where a person would focus within an image intuitively, without requiring precise boundaries or exhaustive markings, making the process fast and practical. During training, the model learns to align its focus with these areas, progressively emphasizing features that carry diagnostic meaning. This guidance works across different types of natural and medical images, including chest X-rays, retinal scans, and dermatological images. Our experiments demonstrate that GFSR achieves comparable or superior accuracy while producing saliency maps that better reflect human expectations. This reduces the reliance on irrelevant patterns and increases confidence in automated diagnostic tools.
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