Year: 2025
Type: conference-paper
Abstract: The rapid increase of Computed Tomography examinations have created a need for robust automated analysis techniques in clinical settings to assist radiologists managing their growing workload. Existing methods generate entire reports directly from 3D CT images, without explicitly focusing on observed abnormalities. This unguided approach can result in repetitive content or incomplete reports. We propose a new anomaly-guided report generation model, which first predicts abnormalities and then generates targeted descriptions for each. Evaluation on a public dataset demonstrates significant improvements in report quality and clinical relevance. We extend our work by conducting an ablation study to demonstrate its effectiveness.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4410295368