In this section
@inproceedings{Fang:2027:10.1007/978-3-032-38062-3_60,
author = {Fang, Y and Zhou, H and Lee, KH and Wang, Y and Zhang, Z and Huang, J and Yang, G},
doi = {10.1007/978-3-032-38062-3_60},
pages = {645--655},
title = {Unleashing Video Language Models for Fine-Grained HRCT Report Generation},
url = {http://dx.doi.org/10.1007/978-3-032-38062-3_60},
year = {2027}
}
TY - CPAPER
AB - Generating precise diagnostic reports from high-resolution computed tomography is critical for clinical workflows, yet remains challenging 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 adapts general-domain VideoLMs to precise report generation. Specifically, AbSteering introduces: (i) an abnormality-centric Chain-of-Thought scheme that encourages finding-first abnormality reasoning, and (ii) a Direct Preference Optimization objective that uses clinically confusable abnormalities as hard negatives to enhance fine-grained discrimination. Our results demonstrate that general-purpose VideoLMs exhibit strong transferability to high-volume medical imaging when guided by AbSteering. Notably, VideoLMs outperform state-of-the-art domain-specific CT foundation models, achieving superior detection sensitivity while mitigating hallucinated findings. Our data and model weights are released at https://github.com/ayanglab/CTRG_VideoLMs.
AU - Fang,Y
AU - Zhou,H
AU - Lee,KH
AU - Wang,Y
AU - Zhang,Z
AU - Huang,J
AU - Yang,G
DO - 10.1007/978-3-032-38062-3_60
EP - 655
PY - 2027///
SN - 0302-9743
SP - 645
TI - Unleashing Video Language Models for Fine-Grained HRCT Report Generation
UR - http://dx.doi.org/10.1007/978-3-032-38062-3_60
ER -
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