Unleashing Video Language Models for Fine-grained HRCT Report Generation

• URL: http://arxiv.org/abs/2603.12469v1
• Date: Thu, 12 Mar 2026 21:34:38 GMT
• Title: Unleashing Video Language Models for Fine-grained HRCT Report Generation
• Abstract summary: AbSteering is an abnormality-centric framework that steers VideoLMs toward precise HRCT report generation.<n>Our results demonstrate that general-purpose VideoLMs possess strong transferability to high-volume medical imaging.
• Score: 15.816436105926542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating precise diagnostic reports from High-Resolution Computed Tomography (HRCT) is critical for clinical workflow, yet it remains a formidable challenge due to the high pathological diversity and spatial sparsity within 3D volumes. While Video Language Models (VideoLMs) have demonstrated remarkable spatio-temporal reasoning in general domains, their adaptability to domain-specific, high-volume medical interpretation remains underexplored. In this work, we present AbSteering, an abnormality-centric framework that steers VideoLMs toward precise HRCT report generation. Specifically, AbSteering introduces: (i) an abnormality-centric Chain-of-Thought scheme that enforces abnormality reasoning, and (ii) a Direct Preference Optimization objective that utilizes clinically confusable abnormalities as hard negatives to enhance fine-grained discrimination. Our results demonstrate that general-purpose VideoLMs possess strong transferability to high-volume medical imaging when guided by this paradigm. Notably, AbSteering outperforms state-of-the-art domain-specific CT foundation models, which are pretrained with large-scale CTs, achieving superior detection sensitivity while simultaneously mitigating hallucinations. Our data and model weights are released at https://anonymous.4open.science/r/hrct-report-generation-video-vlm-728C/

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