# Unleashing Video Language Models for Fine-grained HRCT Report Generation

arXiv: https://arxiv.org/abs/2603.12469 · HTML: https://mlsanity.com/paper/2603.12469/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 22 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Builds on (most cited first, 17 of 17)

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- [RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis](https://mlsanity.com/paper/2404.16754/) (2024)
- [Dia-LLaMA: Towards Large Language Model-driven CT Report Generation](https://mlsanity.com/paper/2403.16386/) (2024)
- [Large Language Model with Region-guided Referring and Grounding for CT Report Generation](https://mlsanity.com/paper/2411.15539/) (2024)
- [Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models](https://mlsanity.com/paper/2404.04936/) (2024)
- [3D-CT-GPT: Generating 3D Radiology Reports through Integration of Large Vision-Language Models](https://mlsanity.com/paper/2409.19330/) (2024)
- [MvKeTR: Chest CT Report Generation with Multi-View Perception and Knowledge Enhancement](https://mlsanity.com/paper/2411.18309/) (2024)

## Abstract (arXiv)

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.