ArticleNPJ digital medicine2025
Hierarchical reasoning for lung cancer detection: from multi-scale perception to hypergraph inference with CR-YOLO.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- PD-YOLO: a high-precision detection network for fine-grained lung cancer and multi-scale pulmonary nodules in CT images.Scientific reports · 2026Article
- Applications, image analysis, and interpretation of computer vision in medical imaging.Frontiers in radiology · 2025Review
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Authors and funding
13 authors.
Funding
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Abstract
Accurate detection of lung cancer from Computed Tomography (CT) scans is vital for improving patient survival but remains challenging for deep learning models, which struggle with scale variations of pulmonary nodules and the complex reasoning required for diagnosis. We propose CR-YOLO, a novel framework incorporating a Cognitive Reasoning C2f (CR-C2f) module that emulates a radiologist's hierarchical workflow. CR-YOLO employs a Multi-scale Convolution (MSC) module for robust feature perception, Global-Local Attention (GLA) Bottlenecks to integrate local morphology with contextual dependencies, and a Hypergraph Convolution (HGC) Refiner for high-order relational inference. Experiments demonstrate that CR-YOLO achieves a mean Average Precision (mAP) of 92.5%, a 4.1% absolute improvement over the YOLOv8n baseline. In addition to improved accuracy, CR-YOLO enhances interpretability through Grad-CAM analysis, highlighting its potential as a reliable and transparent tool for early lung cancer diagnosis.
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Registered trials
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