The paper introduces SCCL-DT, a method that improves zero‑shot chest X‑ray diagnosis by addressing semantic conflicts in contrastive vision‑language pre‑training. It uses adaptive thresholds derived from exponential‑moving‑average statistics to hard‑mask conflicting pairs, and combines two teachers—an inclusive and a consensus rule—through distillation into a single student model. On five public chest X‑ray benchmarks, SCCL‑DT achieves a mean zero‑shot AUC of 0.850, outperforming the best baseline at 0.837.
arXiv:2609.39266v1 Announce Type: new Abstract: Fine-grained vision-language alignment in chest radiography enables zero-shot classification, grounding, and segmentation without task-specific annotat...
Meta-CLIP is a metadata‑conditioned contrastive learning framework that incorporates DICOM acquisition information to reweight cross‑modal negatives during medical vision‑language pretraining. By defining a bounded pairwise margin over numerical and categorical DICOM fields, the method emphasizes distinctions among studies with similar imaging conditions while keeping the kernel fixed during optimization. Evaluations on chest radiography and volumetric CT show that Meta‑CLIP improves zero‑shot classification performance, achieving higher macro‑AUROC scores than baseline CLIP models on multiple benchmarks.
arXiv:2607. 14995v1 Announce Type: new Abstract: Multimodal Contrastive Learning (CL) has shown significant performance in aligning representations across various data modalities and improving downstream tasks, especially in healthcare.
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 paper introduces Similarity-as-Evidence (SaE), a framework that calibrates overconfident Vision‑Language Models (VLMs) for medical active learning by reinterpreting text‑image similarity vectors as evidence and modeling them with a Dirichlet distribution. SaE’s Similarity Evidence Head (SEH) captures uncertainty through vacuity and dissonance, enabling a dual‑factor acquisition strategy that prioritizes high‑vacuity samples early and high‑dissonance samples later. Experiments on ten public medical imaging datasets demonstrate state‑of‑the‑art macro‑averaged accuracy (82.57%) and improved calibration (NLL = 0.425) under a 20% label budget.
AlphaRAD introduces a grounded zero‑shot classification framework for chest radiology that leverages structured medical concepts extracted from reports and a novel α‑Corrected Binary Cross‑Entropy loss to reduce in‑batch noise. It also presents FLaS, a lightweight cross‑modal fusion module that factorizes VLPM representations into independent subspaces, improving spatial grounding without adding parameters. The method achieves state‑of‑the‑art performance on 16 classification benchmarks and sets new records on several grounding, phrase‑grounding, and segmentation datasets.
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...
arXiv:2607. 04478v1 Announce Type: cross Abstract: Automated chest X-ray classification remains challenging due to severe class imbalance, co-occurring pathologies, and the loss of localized features in conventional architectures.
arXiv:2607. 05880v1 Announce Type: cross Abstract: Imaging demand is growing faster than the radiology workforce can expand, and reporting backlogs cannot be resolved through training and recruitment alone.
OmniMed‑FL is a multimodal federated learning framework that fuses chest radiographs and synthetic patient notes to classify five clinical conditions. The study benchmarks eight fusion strategies, three initializations, and four missing‑text imputation rules across 3–20 hospital clients under non‑IID Dirichlet partitioning, showing that federated approaches (FedAvg, FedProx, SCAFFOLD‑AdamW) outperform local‑only training. Multimodal fusion consistently improves performance, achieving macro‑F1 scores up to 0.956 on the synthetic corpus and 0.906 on the radiograph corpus.
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.
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.