Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach
This addresses the problem of efficient and accurate abnormality detection in radiology for clinicians, representing a domain-specific advancement with incremental improvements in methodology and evaluation.
The paper tackled the challenge of automated interpretation of whole-body CT images by proposing a comprehensive taxonomy, dataset, and model (OmniAbnorm-CT) for localizing and describing abnormalities, achieving significant performance improvements over existing methods in internal and external validations across multiple clinical tasks.
Automated interpretation of CT images-particularly localizing and describing abnormal findings across multi-plane and whole-body scans-remains a significant challenge in clinical radiology. This work aims to address this challenge through four key contributions: (i) On taxonomy, we collaborate with senior radiologists to propose a comprehensive hierarchical classification system, with 404 representative abnormal findings across all body regions; (ii) On data, we contribute a dataset containing over 14.5K CT images from multiple planes and all human body regions, and meticulously provide grounding annotations for over 19K abnormalities, each linked to the detailed description and cast into the taxonomy; (iii) On model development, we propose OmniAbnorm-CT, which can automatically ground and describe abnormal findings on multi-plane and whole-body CT images based on text queries, while also allowing flexible interaction through visual prompts; (iv) On evaluation, we establish three representative tasks based on real clinical scenarios, and introduce a clinically grounded metric to assess abnormality descriptions. Through extensive experiments, we show that OmniAbnorm-CT can significantly outperform existing methods in both internal and external validations, and across all the tasks.