A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans

TL;DR

This paper presents a hierarchical deep learning model with slice attention for improved appendicitis detection in 3D CT scans, utilizing external 2D data to enhance small lesion identification and classify disease severity.

Contribution

It introduces a novel hierarchical framework with slice attention mechanisms guided by external datasets, advancing 3D CT-based appendicitis classification.

Findings

AUC improved by 3% for appendicitis

AUC improved by 5.9% for complicated cases

Enhanced detection of small lesions

Abstract

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.