arXiv:2608. 20069v1 Announce Type: new Abstract: While generalist VLMs are expensive to train, creating domain experts is widely assumed to require fine-tuning increasingly large foundation models.
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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.
arXiv:2606. 20477v1 Announce Type: cross Abstract: We study how to train visually grounded vision-language models (VLMs) for radiology without manual spatial annotations.
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.
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...
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...