用3DCT和分层注意力提升阑尾炎分类准确率
A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans
- 引入分层注意力机制，结合2D预训练模型增强小病灶检测
- 复杂阑尾炎分类AUC提升5.9%，总体分类性能优于现有方法
- 适合临床辅助诊断，尤其适用于放射科医生负荷过重场景
及时准确的阑尾炎诊断对预防严重并发症至关重要。尽管CT成像是当前标准诊断工具，但病例数量增长可能导致放射科医生负担过重，引发延误。本文提出一种基于3D CT扫描的深度学习模型，通过外部2D数据集引导的切片注意力机制，提升小病灶检测能力；同时构建分层分类框架，利用预训练2D模型区分简单与复杂阑尾炎。实验表明，该方法在阑尾炎分类上提升AUC 3%，复杂阑尾炎分类提升5.9%，相比之前工作提供更高效可靠的诊断方案。
原文摘要 · Abstract (English)
Timely and accurate diagnosis of appendicitis is critical in clinical settings to prevent serious complications. While CT imaging remains the standard diagnostic tool, the growing number of cases can overwhelm radiologists, potentially causing delays. In this paper, we propose a deep learning model that leverages 3D CT scans for appendicitis classification, incorporating Slice Attention mechanisms guided by external 2D datasets to enhance small lesion detection. Additionally, we introduce a hierarchical classification framework using pre-trained 2D models to differentiate between simple and complicated appendicitis. Our approach improves AUC by 3% for appendicitis and 5.9% for complicated appendicitis, offering a more efficient and reliable diagnostic solution compared to previous work.
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