ArticleNature communications2024
A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Innovative technologies and their clinical prospects for early lung cancer screening.Clinical and experimental medicine · 2025Pooled it
- AI-Assisted segmentation and volumetric reconstruction of radiographs through multi-angular scintillation imaging.Nature communications · 2026Article
- RoentMod: a synthetic chest X-ray modification model to identify and correct image interpretation model shortcuts.NPJ digital medicine · 2026Article
- PulmoX-Net: a channel-attention enhanced deep learning model for multi-class pulmonary pathology classification in chest radiography.Frontiers in medicine · 2026Article
- EQISP: efficient quantized image signal processing with multi-scale pyramid fusion for resource constrained embodied perception.Frontiers in neurorobotics · 2026Article
- Can a Natural Image-Based Foundation Model Outperform a Retina-Specific Model in Detecting Ocular and Systemic Diseases?Ophthalmology science · 2026Article
- Localization and Classification of Adrenal Masses in Multiphase Computed Tomography: Retrospective Study.Journal of medical Internet research · 2025Article
- YOLOv11-MFF: A multi-scale frequency-adaptive fusion network for enhanced CXR anomaly detection.PloS one · 2025Article
- Weakly supervised chest X-ray abnormality localization with non-linear modulation and foreground control.Scientific reports · 2024Article
- Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning-based classification of anti-personnel mines and sub-gram metal content in mineralized soil (DL-MMD).Scientific reports · 2024Article
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38 authors.
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Abstract
Accurate identification and localization of multiple abnormalities are crucial steps in the interpretation of chest X-rays (CXRs); however, the lack of a large CXR dataset with bounding boxes severely constrains accurate localization research based on deep learning. We created a large CXR dataset named CXR-AL14, containing 165,988 CXRs and 253,844 bounding boxes. On the basis of this dataset, a deep-learning-based framework was developed to identify and localize 14 common abnormalities and calculate the cardiothoracic ratio (CTR) simultaneously. The mean average precision values obtained by the model for 14 abnormalities reached 0.572-0.631 with an intersection-over-union threshold of 0.5, and the intraclass correlation coefficient of the CTR algorithm exceeded 0.95 on the held-out, multicentre and prospective test datasets. This framework shows an excellent performance, good generalization ability and strong clinical applicability, which is superior to senior radiologists and suitable for routine clinical settings.
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