Seeing Like Radiologists: Context- and Gaze-Guided Vision-Language Pretraining for Chest X-rays
Despite recent advances in medical vision-language pretraining, existing models still struggle to capture the diagnostic workflow: radiographs are typically treated as context-agnostic images, while radiologists' gaze -- a crucial cue for visual reasoning -- remains largely underexplored by existing methods. These limitations hinder the modeling of disease-specific patterns and weaken cross-modal alignment. To bridge this gap, we introduce CoGaze, a Context- and Gaze-guided vision-language pretr
Record details
Published: 27 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
QU-NLP at ArchEHR-QA 2026: Two-Stage QLoRA Fine-Tuning of Qwen3-4B for Patient-Oriented Clinical Question Answering and Evidence Sentence Alignment
arXiv · 26 March 2026
On the Spectral Geometry of Cross-Modal Representations: A Functional Map Diagnostic for Multimodal Alignment
arXiv · 28 March 2026
SHAPE: Structure-aware Hierarchical Unsupervised Domain Adaptation with Plausibility Evaluation for Medical Image Segmentation
arXiv · 23 March 2026
Cycle Inverse-Consistent TransMorph: A Balanced Deep Learning Framework for Brain MRI Registration
arXiv · 23 March 2026
Behavioural feasible set: Value alignment constraints on AI decision support
arXiv · 22 March 2026
Physiological and Semantic Patterns in Medical Teams Using an Intelligent Tutoring System
arXiv · 31 March 2026
How to cite this record
ethics.ai (27 March 2026), “Seeing Like Radiologists: Context- and Gaze-Guided Vision-Language Pretraining for Chest X-rays,” evidence record 6679, https://ethics.ai/record/6679 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.