arXiv:2510. 15042v3 Announce Type: replace-cross Abstract: In the 3D medical image domain, vision-language pre-training is used to create vision-language encoders (VLEs) that can support radiologists by retrieving patients with similar abnormalities, predicting likelihoods of abnormality, or, with downstream adaptation, generating radiological reports.
By Tassilo Wald, Ibrahim Ethem Hamamci, Yuan Gao, Sam Bond-Taylor, Harshita Sharma, Maximilian Ilse, Cynthia Lo, Olesya Melnichenko, Anton Schwaighofer, Noel C. F. Codella, Maria Teodora Wetscherek, Klaus H. Maier-Hein, Panagiotis Korfiatis, Valentina Salvatelli, Javier Alvarez-Valle, Fernando P\'erez-Garc\'ia
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.
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...
By Qixing Zhao, Jinpeng Li
arXiv:2607. 27154v1 Announce Type: cross Abstract: CT vision-language foundation models have demonstrated promising performance across downstream tasks, but are typically trained with whole-volume representations that dilute fine-grained anatomical signals.
By Roshan Kenia, Stephanie L McNamara, William Lotter
arXiv:2607. 27154v2 Announce Type: replace-cross Abstract: CT vision-language foundation models have demonstrated promising performance across downstream tasks, but are typically trained with whole-volume representations that dilute fine-grained anatomical signals.
By Roshan Kenia, Stephanie L McNamara, William Lotter
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.
By Jianzhong You, Yuan Gao, Chris McIntosh