Instance-Guided Report Anchoring (IGRA) is a model-agnostic module that links each abnormality instance in a chest CT to the corresponding finding in a radiology report during training, while discarding text components at inference. By reformulating free-text grounding as multi-label volumetric segmentation, IGRA allows all abnormality categories to be predicted in a single image-only forward pass. The method improves Dice scores by 22.5% over the strongest image-only baseline and matches state‑of‑the‑art performance on single-finding subsets, with consistent gains across multiple 3D segmentation backbones and datasets.
By Zhenyu Bu, Haoyan Ding, Chushu Shen, Xinyuan Zheng, Peiyu Duan, Xueqi Guo, Sepehr Farhand, Yoshihisa Shinagawa, Gerardo Hermosillo, Chaowei Wu
arXiv:2606. 17710v1 Announce Type: cross Abstract: Medical vision-language models report strong chest radiograph accuracy, and this is increasingly read as evidence that they use the image.
By Mahshad Lotfinia, Sebastian Ziegelmayer, Lisa Adams, Daniel Truhn, Andreas Maier, Soroosh Tayebi Arasteh
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:2604.09537v2 Announce Type: replace
Abstract: A grounding verifier checks a statement about a patient's image against the radiology report and the cited evidence. The state-of-the-art methods f...
By Soroosh Tayebi Arasteh, Mehdi Joodaki, Mahshad Lotfinia, Tri-Thien Nguyen, Sebastian Ziegelmayer, Sven Nebelung, Daniel Truhn
arXiv:2608. 07299v1 Announce Type: cross Abstract: Radiology reports describe clinical observations but do not specify executable segmentation targets.
By Chengyi Peng, Haoyu Yang, Meixing Shi, Yuxiang Cai, Yankai Jiang
arXiv:2605.30984v2 Announce Type: replace-cross
Abstract: Modern 3D medical vision-language models (VLMs) can generate fluent radiology-style text while exhibit critically low pathology detection and...
By Tom Maye-Lasserre, Yitong Li, Bailiang Jian, Morteza Ghahremani, Benedikt Wiestler, Christian Wachinger
arXiv:2608.30758v1 Announce Type: new
Abstract: Recent radiology multi-modal language models have made substantial progress in chest X-ray report generation, visual question answering, and temporal r...
By Adonay Demewez Gebremedhin, Wessam Shehieb, Sara Alansari, Mohamad Alansari, Muzammal Naseer, Sajid Javed, Naoufel Werghi
arXiv:2608.28455v1 Announce Type: new
Abstract: Contrastive vision-language learning uses paired chest CT volumes and radiology reports to learn abnormality classifiers without manually annotated lab...
By Huseyin Umut Isik, Mehmet Alp Ozaydin, Sila Kurugol, \c{S}eyda Ertekin
arXiv:2609.13238v1 Announce Type: new
Abstract: Maxillofacial report generation from cone beam computed tomography is scored here by a composite objective placing 80% of its weight on a large languag...
By Ajo Babu George, Govind Arun, Sidharth N Krishna, Uma Ranjan
Purpose: To evaluate whether large language model (LLM)-assisted label cleaning can identify label-report discordance in CT-RATE, a large-scale public chest CT dataset. Materials and Methods: After report-level deduplication, 24,446 unique radiology reports were identified.
APED-CXR is a compact chest X‑ray model that localizes findings using only image input at test time. It distills spatial evidence from a frozen multimodal teacher—trained on radiographs and reports—into a 11.1 M‑parameter student, which predicts diseases and bounding boxes from shared evidence maps. Training with teacher‑guided proposal refinement improves box‑level localization F1 from 0.302 to 0.338 on PadChest‑GR and raises recall on NIH ChestX-ray14 from 0.423 to 0.447, while preserving diagnostic AUC.
By Md Shihabul Islam Shovo, Nishi Kanta Paul, Jamil Fayyad
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)