ArticleScientific data2024
BM-BronchoLC - A rich bronchoscopy dataset for anatomical landmarks and lung cancer lesion recognition.
Article in Scientific data, 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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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Remimazolam compared with propofol, dexmedetomidine, and midazolam for adult sedation in flexible bronchoscopy: a systematic review and meta-analysis.Brazilian journal of anesthesiology (Elsevier)Pooled it
- BronchoLumen: analysis of recent YOLO-based architectures for real-time bronchial orifice detection in video bronchoscopy.International journal of computer assisted radiology and surgery · 2026Article
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Artificial Intelligence in Pulmonary Endoscopy: Current Evidence, Limitations, and Future Directions.Journal of imaging · 2026Review
- Towards Automated Reporting: A Bronchoscopy Report Dataset for Enhancing Multimodality Large Language Models.Scientific data · 2026Article
- Multi-adapter SAM-inspired bronchoscopic image segmentation for lung cancer diagnosis.Frontiers in oncology · 2026Article
- Human and Deep Learning Predictions of Peripheral Lung Cancer Using a 1.3 mm Video Endoscopic Probe.Respirology (Carlton, Vic.) · 2025Article
- Review
- BronchoGAN: anatomically consistent and domain-agnostic image-to-image translation for video bronchoscopy.International journal of computer assisted radiology and surgery · 2025Article
- Kidney Endoscopy Video to Preoperative CT Alignment for Depth Estimation.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- BM-BronchoLC - A rich bronchoscopy dataset for anatomical landmarks and lung cancer lesion recognition.Scientific data · 2024Article
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Authors and funding
31 authors at 3 institutions in 1 country.
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Abstract
Flexible bronchoscopy has revolutionized respiratory disease diagnosis. It offers direct visualization and detection of airway abnormalities, including lung cancer lesions. Accurate identification of airway lesions during flexible bronchoscopy plays an important role in the lung cancer diagnosis. The application of artificial intelligence (AI) aims to support physicians in recognizing anatomical landmarks and lung cancer lesions within bronchoscopic imagery. This work described the development of BM-BronchoLC, a rich bronchoscopy dataset encompassing 106 lung cancer and 102 non-lung cancer patients. The dataset incorporates detailed localization and categorical annotations for both anatomical landmarks and lesions, meticulously conducted by senior doctors at Bach Mai Hospital, Vietnam. To assess the dataset's quality, we evaluate two prevalent AI backbone models, namely UNet++ and ESFPNet, on the image segmentation and classification tasks with single-task and multi-task learning paradigms. We present BM-BronchoLC as a reference dataset in developing AI models to assist diagnostic accuracy for anatomical landmarks and lung cancer lesions in bronchoscopy data.
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