The paper introduces V-REX, a specialist vision‑language model (VLM) tailored for veterinary X‑ray interpretation. By redesigning the VLM pipeline—including text tokenisation, pre‑training, grounding, and inference—the authors demonstrate that a carefully engineered model can outperform larger generalist foundation models while using far fewer parameters, data, and compute. V-REX is the first VLM capable of generating diagnostic reports for veterinary radiographs, achieving significant gains over open foundation models on this task.
arXiv:2606. 19460v1 Announce Type: cross Abstract: We introduce the first generative foundation model for chest radiograph synthesis trained from scratch at the billion-parameter scale.
By Fabio De Sousa Ribeiro, Emma A. M. Stanley, Charles Jones, Tian Xia, Dominic C. Marshall, Laurent Renard Trich\'e, Christopher V. Cosgriff, Panagiotis Dimitrakopoulos, Sotirios A. Tsaftaris, Ben Glocker
arXiv:2606. 20477v1 Announce Type: cross Abstract: We study how to train visually grounded vision-language models (VLMs) for radiology without manual spatial annotations.
By Yusuf Salcan (Computer Vision Group, University of Freiburg, Germany, CRIION-AI Lab, Freiburg, Germany), Simon Ging (Computer Vision Group, University of Freiburg, Germany, Adaptive & Agentic AI), Robin Schirrmeister (Department of Radiology, Medical Center -- University of Freiburg, Germany), Philipp Arnold (Department of Radiology, Medical Center -- University of Freiburg, Germany), Elmar Kotter (Department of Radiology, Medical Center -- University of Freiburg, Germany), Behzad Bozorgtabar (Adaptive & Agentic AI), Thomas Brox (Computer Vision Group, University of Freiburg, Germany)
arXiv:2608. 03890v1 Announce Type: cross Abstract: A clinically useful chest X-ray system must go beyond fluent report generation: it should classify findings with tunable decision thresholds, localize them spatially, and derive the anatomical measurements upon which many diagnoses depend.
By Mercy Prasanna Ranjit, Anirban Porya, Sathvik Joel, Niharika Vadlamudi, Nikhilesh Chowdary Eathamukkala, Prasanth V V, Abhyuday Kumara Swamy, Pranay Narhari Umredkar, Pradeep Narayan, Vivek Rajagopal, Tanuja Ganu
arXiv:2608.30021v1 Announce Type: cross
Abstract: Errors in radiology reports can adversely affect patient treatment, yet automated report quality assurance remains challenging because errors are oft...
By Hermione Warr, Harry Anthony, Lilli J Freischem, Yasin Ibrahim, Daniel R McGowan, Konstantinos Kamnitsas
arXiv:2610.08813v1 Announce Type: new
Abstract: X-ray image-based Radiology Report Generation (RRG) constitutes a critical research direction within medical artificial intelligence, with great potent...
By Xiao Wang, Yuxiang Zhang, Dan Xu, Yuehang Li, Shiao Wang, Bo Jiang, Yaowei Wang, Yonghong Tian, Jin Tang
arXiv:2506.10633v2 Announce Type: replace
Abstract: Latent Diffusion Models have shown remarkable results in text-guided image synthesis in recent years. In the domain of natural (RGB) images, recent...
By Konstantinos Vilouras, Ilias Stogiannidis, Junyu Yan, Alison Q. O'Neil, Sotirios A. Tsaftaris
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.
The study benchmarks foundation models for chest X‑ray classification across a domain shift from adult to pediatric patients. Six foundation models and two CNN baselines were evaluated on a harmonized five‑label task using adult data for adaptation and tested on both adult and pediatric sets. Results show all models experience a substantial performance drop—over 10 percentage points—when applied to pediatric data, and deeper adaptation does not markedly improve cross‑population generalization.
By Sara In\'acio, Hugo Proen\c{c}a, Jo\~ao C. Neves
arXiv:2603.02790v2 Announce Type: replace
Abstract: Foundation models are changing the way we develop medical artificial intelligence. By learning broadly generalizable features across diverse data m...
By Michelle Stegeman (and on behalf of the UNICORN consortium), Lena Philipp (and on behalf of the UNICORN consortium), Fennie van der Graaf (and on behalf of the UNICORN consortium), Marina D'Amato (and on behalf of the UNICORN consortium), Cl\'ement Grisi (and on behalf of the UNICORN consortium), Luc Builtjes (and on behalf of the UNICORN consortium), Joeran S. Bosma (and on behalf of the UNICORN consortium), Judith Lefkes (and on behalf of the UNICORN consortium), Rianne A. Weber (and on behalf of the UNICORN consortium), James A. Meakin (and on behalf of the UNICORN consortium), Thomas Koopman (and on behalf of the UNICORN consortium), Anne Mickan (and on behalf of the UNICORN consortium), Mathias Prokop (and on behalf of the UNICORN consortium), Ewoud J. Smit (and on behalf of the UNICORN consortium), Fr\'ed\'erique Meeuwsen (and on behalf of the UNICORN consortium), Geert Litjens (and on behalf of the UNICORN consortium), Jeroen van der Laak (and on behalf of the UNICORN consortium), Bram van Ginneken (and on behalf of the UNICORN consortium), Maarten de Rooij (and on behalf of the UNICORN consortium), Henkjan Huisman (and on behalf of the UNICORN consortium), Colin Jacobs (and on behalf of the UNICORN consortium), Francesco Ciompi (and on behalf of the UNICORN consortium), Alessa Hering (and on behalf of the UNICORN consortium)
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:2603.15525v4 Announce Type: replace
Abstract: Deep learning models for chest X-ray diagnosis are constrained by limited coverage of clinically meaningful concept combinations in publicly availa...
By Amy Rafferty, Rishi Ramaesh, Ajitha Rajan