arXiv:2601.15891v4 Announce Type: replace
Abstract: Vision-language pretraining has driven progress in medical image representation learning, but it depends on paired image-text data and can inherit...
By Anas Anwarul Haq Khan, Mariam Husain, Pratik Jalan, Kshitij Jadhav
arXiv:2610.28289v1 Announce Type: new
Abstract: Radiologists interpret follow-up chest X-rays by comparing current and prior examinations to identify new, resolved or progressing findings. Most repor...
By Sike Xiang, Shuang Chen, Julie Cox, Amir Atapour-Abarghouei
arXiv:2603.11938v2 Announce Type: replace-cross
Abstract: Structured radiology reporting promises faster, more consistent communication than free text, but automation remains difficult as models must...
By Chantal Pellegrini, Adrian Delchev, Ege \"Ozsoy, Nassir Navab, Matthias Keicher
arXiv:2409.16183v2 Announce Type: replace
Abstract: Radiology is a vital and complex component of modern clinical workflow and covers many tasks. Recently, vision-language (VL) foundation models in m...
By Xiaohong Liu, Guoxing Yang, Yulin Luo, Jiaji Mao, Xiang Zhang, Haibo Wang, Zhiyang He, Ming Gao, Shanghang Zhang, Jun Shen, Guangyu Wang
Current evaluation protocols for Vision-Language Models (VLMs) in Radiology Report Generation (RRG) rely on report-level metrics that measure lexical overlap or aggregate clinical correctness. However, such metrics do not test whether individual diagnostic statements stem from the actual pathological evidence visible in the image.
Vision-language pre-training (VLP) holds great promise for general-purpose medical AI by leveraging radiology reports as rich textual supervision, yet existing methods struggle with 3D CT imaging due to inefficient visual backbones and coarse semantic alignment. To address these issues, we propose a tailored VLP framework featuring three key components: (1) a CNN-ViT hybrid encoder that replaces ViT's patch embedding with a 3D CNN backbone to efficiently capture local anatomical details while preserving global attention and compatibility with pre-trained cross-modal priors; (2) a disease-level contrastive learning mechanism using learnable query tokens to dynamically extract disease-specific semantics from full reports and align them with corresponding visual features, thereby disentangling distinct diseases within the same anatomical region; and (3) a diagnosis-aware prompt strategy that employs real clinical phrases and aggregated disease prototypes to bridge the pre-training-inference gap and enhance zero-shot diagnostic reliability.