用深度学习同时检测肠梗阻并定位关键过渡区，提升诊断效率。
Bowel Obstruction Detection and Localization on Abdominal CT with Deep Learning
- 多任务框架联合检测梗阻与定位过渡区
- 检测准确率93%，过渡区定位命中率95%
- 可解释性设计让模型聚焦关键区域，适合临床辅助
肠梗阻是常见且可能危及生命的消化道疾病。面对日益增长的诊断工作量，基于CT扫描的自动化肠梗阻诊断可加速识别过程，改善患者预后。本文提出一种深度学习框架，采用多任务目标，联合完成肠梗阻检测与过渡区定位。此外，通过引入内在可解释的分类方法，在单张切片内定位疑似过渡点，该方法通过学习概率选择掩码，使分类器预测仅依赖于小范围图像区域。在包含1,427例腹部CT的内部数据集上评估，模型在梗阻检测测试中达到93%准确率，过渡区定位的Hit@10达95%。作为首个能可靠定位过渡区的方法，本研究标志着向自动识别这一关键临床标志迈出重要一步。
原文摘要 · Abstract (English)
Bowel obstruction is a common and potentially life-threatening gastrointestinal condition. In the face of rising diagnostic workloads, the automated diagnosis of bowel obstruction on CT scans supports radiologists by accelerating detection and improving patient outcomes. In this work, we propose a deep learning framework with a multi-task objective that jointly detects bowel obstruction and localizes its transition zone. Additionally, we extend the method with an inherently interpretable classification method that locates the suspected transition point within a slice. It does so by learning a probabilistic selection mask that faithfully bases the classifier's prediction solely on a small image region. The proposed method is evaluated on an internal dataset comprising 1,427 abdominal CTs. Here, the model achieves an obstruction detection test accuracy of 93% and a Hit@10 transition zone localization of 95%. As the first method to reliably localize the transition zone, this marks a significant step towards the automated identification of this critical clinical landmark.
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