arXiv:2608. 07299v1 Announce Type: cross Abstract: Radiology reports describe clinical observations but do not specify executable segmentation targets.
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.
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.
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...
arXiv:2505.03380v2 Announce Type: replace Abstract: Accurate delineation of tumors and surrounding organs-at-risk is essential for radiotherapy, surgery and treatment response assessment, yet remains...
arXiv:2604. 27720v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly applied to medical visual question answering (Med-VQA), yet whether they can \emph{localize} the evidence behind their answers---a prerequisite for clinical auditability---is poorly characterized.
The paper introduces Clinical Intent Extraction (CIE), a task that transforms fragmented clinical action annotations into complete structured records called Clinical Intent Representation (CIR). CIR decomposes each action into verb, type, coded target, timing, condition, request‑intent (aligned to HL7 FHIR) and modality, adding dimensions absent in prior datasets. By re‑expressing five heterogeneous corpora into CIR, the authors create CIRCA, a benchmark of 10,011 harmonized intents with human‑validated subsets, crosswalks, and a deterministic FHIR R4 mapper, and demonstrate that existing models perform poorly on the full task, highlighting the need for targeted development.
arXiv:2608. 10505v1 Announce Type: new Abstract: Automated radiology report generation is advancing rapidly in response to the shortage of radiologists, yet unlike a perception model, existing generation models offer no control over the sensitivity-specificity trade-off of their diagnostic content.
arXiv:2606. 28392v1 Announce Type: cross Abstract: Accurate lesion segmentation in PET/CT is critical for oncology, yet remains challenging because physiologic tracer uptake and artifacts can mimic malignant signal.
The paper introduces Report Supervision (R‑Super), a framework that uses radiology reports to supervise tumor segmentation models. By incorporating loss functions that align segmentation outputs with report‑derived tumor counts, sizes, and locations, R‑Super improves detection and segmentation performance. Experiments on kidney and pancreatic tumors show up to a 15% increase in F1‑Score and DSC compared to mask‑only training, outperforming methods like CLIP and multi‑task learning.
Med-AR introduces two autoregressive vision‑language models, Med‑AR‑8B and Med‑AR‑2B, pretrained on structured radiology reports, abnormality‑focused text, and region annotations to address long‑tailed chest X‑ray classification. The models outperform existing contrastive, self‑supervised, and supervised encoders—including Med‑CLIP, CheXFound, EVA‑Base, ARK, and BioViL‑T—across PadChest, MIMIC‑CXR, and CheXpert, achieving higher mean AUROC and AUPRC for head, medium, and tail findings and lower excess area under the risk‑coverage curve. Med‑AR also demonstrates improved selective‑prediction performance, with Med‑AR‑8B raising tail‑label mean AUPRC on MIMIC‑CXR from 0.1033 to 0.1441 and Med‑AR‑2B delivering the strongest discrimination on PadChest.
SeGDeP introduces a decoupled prompt system that separates semantic identification from spatial grounding in reasoning segmentation. It uses a semantic prompt branch and an independent geometric projection path to produce semantic features and a DETR‑predicted box, which jointly condition a SAM mask decoder. The model achieves high cIoU and gIoU scores while adapting only a small fraction of Qwen3‑VL parameters via LoRA.
arXiv:2606. 17062v1 Announce Type: cross Abstract: Radiology report evaluation must distinguish clinical compatibility from surface similarity, because negation, laterality, or normal-abnormal polarity can reverse a finding.