A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans
This addresses the challenge of radiologist overload in clinical settings for appendicitis diagnosis, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackled the problem of timely and accurate diagnosis of appendicitis from 3D CT scans by proposing a deep learning model with slice attention and hierarchical classification, resulting in AUC improvements of 3% for appendicitis and 5.9% for complicated appendicitis.
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.