Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification
Journal:
arXiv
Published Date:
Mar 26, 2025
Abstract
The rapid increase in the number of Computed Tomography (CT) scan
examinations has created an urgent need for automated tools, such as organ
segmentation, anomaly classification, and report generation, to assist
radiologists with their growing workload. Multi-label classification of
Three-Dimensional (3D) CT scans is a challenging task due to the volumetric
nature of the data and the variety of anomalies to be detected. Existing deep
learning methods based on Convolutional Neural Networks (CNNs) struggle to
capture long-range dependencies effectively, while Vision Transformers require
extensive pre-training, posing challenges for practical use. Additionally,
these existing methods do not explicitly model the radiologist's navigational
behavior while scrolling through CT scan slices, which requires both global
context understanding and local detail awareness. In this study, we present
CT-Scroll, a novel global-local attention model specifically designed to
emulate the scrolling behavior of radiologists during the analysis of 3D CT
scans. Our approach is evaluated on two public datasets, demonstrating its
efficacy through comprehensive experiments and an ablation study that
highlights the contribution of each model component.
