分阶段检测胃肠道出血，提升诊断准确率
Divide and Conquer: Grounding a Bleeding Areas in Gastrointestinal Image with Two-Stage Model
- 先分类后定位，避免任务间干扰
- 在Auto-WCEBleedGen数据集上获第二名
- 适合医疗影像分析与临床辅助诊断
准确检测和分割胃肠道出血对诊断消化性溃疡和结直肠癌等疾病至关重要。本研究提出一种两阶段框架，将分类与定位任务解耦，以应对传统多任务学习模型联合优化分类与分割所面临的固有挑战。该方法首先将图像分类为出血或非出血，从而隔离后续定位任务，减少任务间干扰和标签异质性影响。为进一步提升性能，引入随机权重平均（Stochastic Weight Averaging）与测试时增强（Test-Time Augmentation），增强模型对领域偏移和标注不一致的鲁棒性。该方法在Auto-WCEBleedGen Challenge V2数据集上取得第二名成绩。实验表明，在具有稳定视觉模式的序列数据上，分类准确率和分割精度均有显著提升。本研究展示了两阶段策略在医学图像分析中的实际价值，为胃肠道出血检测与分割设立了新标准。代码已公开于GitHub。
原文摘要 · Abstract (English)
Accurate detection and segmentation of gastrointestinal bleeding are critical for diagnosing diseases such as peptic ulcers and colorectal cancer. This study proposes a two-stage framework that decouples classification and grounding to address the inherent challenges posed by traditional Multi-Task Learning models, which jointly optimizes classification and segmentation. Our approach separates these tasks to achieve targeted optimization for each. The model first classifies images as bleeding or non-bleeding, thereby isolating subsequent grounding from inter-task interference and label heterogeneity. To further enhance performance, we incorporate Stochastic Weight Averaging and Test-Time Augmentation, which improve model robustness against domain shifts and annotation inconsistencies. Our method is validated on the Auto-WCEBleedGen Challenge V2 Challenge dataset and achieving second place. Experimental results demonstrate significant improvements in classification accuracy and segmentation precision, especially on sequential datasets with consistent visual patterns. This study highlights the practical benefits of a two-stage strategy for medical image analysis and sets a new standard for GI bleeding detection and segmentation. Our code is publicly available at this GitHub repository.
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