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.
arXiv:2608. 09774v1 Announce Type: cross Abstract: Thoracic pathologies rarely occur in isolation, yet standard multi-label classifiers rely on shared global descriptors, discarding \emph{where} findings lie and \emph{how} they co-occur.
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.
arXiv:2608. 00147v1 Announce Type: cross Abstract: Vision-language pretraining learns rich medical image representations from radiology reports, but previous model variants commonly operate within a single shared embedding space, so concept-level structure and interpretability must be recovered post hoc, limiting model transparency and, hence, clinical utility.
The study introduces GazeRIB, a method that uses radiologists’ gaze data to mask unseen regions during training of chest X‑ray models. By comparing panels of three, five, and eight readers against individual readers across multiple architectures, the authors found that panels consistently improved macro F1 scores by 5.33–7.47 percentage points, with three readers capturing most of the benefit. External validation showed panel‑based models also enhanced ROC‑AUC on the SIIM‑FISABIO‑RSNA dataset, though results varied on other cohorts.
MultiViewDx is a physician‑validated multimodal instruction dataset that links medical imaging studies with patient context and normalizes heterogeneous reports into an evidence‑linked workflow (evidence → findings → differential discussion → diagnosis). The dataset covers a wide range of imaging modalities and uses a unified image‑text retriever to ensure that instruction synthesis is grounded in source‑supported evidence. Fine‑tuned models on MultiViewDx achieve the highest average accuracy on four MedVQA benchmarks and receive the strongest overall rating on JAMA Clinical Challenge cases, with ablations confirming the importance of case‑level multi‑view organization and evidence‑linked reasoning.
The study investigates when adding zero‑shot pseudo‑labels from vision‑language models (VLMs) to small expert‑labelled medical imaging datasets ceases to improve, and may even degrade, classifier performance. Across three benchmarks—PatchCamelyon, ISIC 2019, and NIH ChestX‑ray14—the crossover point occurs after roughly 20–50, 100, and 250–500 expert labels, respectively, with additional pseudo‑labels reducing macro‑AUC by up to 0.14 points. The analysis shows that the VLM’s labeling quality, rather than the student model size, limits performance, and a simple bias‑variance rule can predict the benefit of pseudo‑labels in most settings.
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.
CheXPoT is a post‑hoc visual verification framework that refines vision‑language model (VLM) generated chest radiograph reports by decomposing them into predicates, grounding each against DenseNet‑121 and Grad‑CAM evidence, and removing unsupported findings. On a held‑out set of 4,145 images, CheXPoT significantly improves precision and F1 scores across multiple CheXpert classes, boosting micro‑average precision from 19.2 % to 46.8 % and micro‑average F1 from 23.0 % to 49.4 %. The method also enhances performance for other VLM generators such as LLaVA‑Med and MAIRA‑2, demonstrating that inexpensive visual verification can correct hallucinations and improve report quality.
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.
arXiv:2608.30467v1 Announce Type: new Abstract: Deep-learning models can achieve strong chest X-ray (CXR) classification performance without establishing whether their predictions predominantly rely...
arXiv:2608. 16709v1 Announce Type: cross Abstract: A radiologist reading a model's output faces two problems.
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...