arXiv Machine Learning
CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographs
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arXiv AI
Sep 25
Clinical Knowledge Graphs for Chest X-Ray Device Reasoning
The paper introduces an uncertainty‑aware clinical knowledge graph for chest X‑ray device reasoning, capturing device instances, tip estimates, placement assessments, provenance, report events, and temporal links as interconnected evidence. The graph builder processes 30,083 studies from 3,255 patients, producing 914,632 evidence nodes and 884,549 typed relationships, while preserving detailed uncertainty and provenance information for each predicted device. The authors also outline typed data contracts, uncertainty representations, abstention rules, report‑image grounding, and longitudinal query mechanisms, though the current analysis is post‑hoc descriptive and does not yet demonstrate clinical utility.
Hugging Face Trending Papers
Jul 30
CXR-Retrieve: Compositional Text-to-Image Retrieval in Chest Radiography
Large chest radiography archives are difficult to search because most studies are paired only with free-text reports rather than structured clinical annotations. Vision-language models offer a natural interface for text-to-image retrieval, but current biomedical models are primarily optimized for report-to-image matching rather than for satisfying short clinical search queries.
arXiv Computer Vision
Sep 21
Graph-Augmented Topological Internalization with Dual-Stream Classifiers for Medical Report Generation
The paper introduces GDMRG, a Graph-Augmented Dual-Stream Medical Report Generation framework that incorporates a Topological Knowledge Internalization module using a Graph Convolutional Network to encode disease co-occurrence priors. It employs a dual-stream classifier—one branch generating diagnostic prompts under topological constraints and an auxiliary branch dynamically calibrating decision boundaries for imbalanced samples—alongside a Diagnosis-Guided Spatial Attention mechanism to align visual features with clinical semantics. Experiments on MIMIC-CXR show competitive clinical efficacy and natural language fluency, with strong zero-shot performance on IU X-Ray.
arXiv AI
Aug 13
ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation
arXiv:2411. 15122v2 Announce Type: replace-cross Abstract: AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays.
arXiv AI
Jun 19
Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers
arXiv:2606. 19460v1 Announce Type: cross Abstract: We introduce the first generative foundation model for chest radiograph synthesis trained from scratch at the billion-parameter scale.
arXiv Computer Vision
4d ago
ZAGNet: Zone-Aware Graph Aggregation Network for Patient-Level Lung Ultrasound Diagnosis
ZAGNet is a Zone‑Aware Graph Neural Network that models temporally tracked lung ultrasound findings as graph nodes linked by anatomical zone adjacency, enabling contextual propagation via a graph transformer and a virtual global node for patient‑level diagnosis. It handles missing zones by operating on a flexible graph structure, and outperforms traditional max/mean pooling on a multicenter dataset of 714 subjects, achieving AUCs of 0.803 for consolidation and 0.893 for pleural effusion. The study demonstrates that graph‑based inter‑zone reasoning improves automated patient‑level LUS assessment.