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Vu, Van Giap; Hoang, Anh Duc; Phan, Thu Phuong; Nguyen, Ngoc Du; Nguyen, Thanh Thuy; Nguyen, Duc Nghia; Dao, Ngoc Phu; Doan, Thi Phuong Lan; Nguyen, Thi Thanh Huyen; Trinh, Thi Huong; Pham, Thi Le Quyen; Le, Thi Thu Trang; Thi Hanh, Phan; Pham, Van Tuyen; Tran, Van Chuong; Vu, Dang Luu; Tran, Van Luong; Nguyen, Thi Thu Thao; Pham, Cam Phuong; Pham, Gia Linh; Luong, Son Ba; Pham, Trung-Dung; Nguyen, Duy-Phuc; Truong, Thi Kieu Anh; [...]

BM-BronchoLC - A rich bronchoscopy dataset for anatomical landmarks and lung cancer lesion recognition

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  • Medientyp: E-Artikel
  • Titel: BM-BronchoLC - A rich bronchoscopy dataset for anatomical landmarks and lung cancer lesion recognition
  • Beteiligte: Vu, Van Giap; Hoang, Anh Duc; Phan, Thu Phuong; Nguyen, Ngoc Du; Nguyen, Thanh Thuy; Nguyen, Duc Nghia; Dao, Ngoc Phu; Doan, Thi Phuong Lan; Nguyen, Thi Thanh Huyen; Trinh, Thi Huong; Pham, Thi Le Quyen; Le, Thi Thu Trang; Thi Hanh, Phan; Pham, Van Tuyen; Tran, Van Chuong; Vu, Dang Luu; Tran, Van Luong; Nguyen, Thi Thu Thao; Pham, Cam Phuong; Pham, Gia Linh; Luong, Son Ba; Pham, Trung-Dung; Nguyen, Duy-Phuc; Truong, Thi Kieu Anh; [...]
  • Erschienen: Springer Science and Business Media LLC, 2024
  • Erschienen in: Scientific Data, 11 (2024) 1
  • Sprache: Englisch
  • DOI: 10.1038/s41597-024-03145-y
  • ISSN: 2052-4463
  • Schlagwörter: Library and Information Sciences ; Statistics, Probability and Uncertainty ; Computer Science Applications ; Education ; Information Systems ; Statistics and Probability
  • Entstehung:
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  • Beschreibung: AbstractFlexible 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.
  • Zugangsstatus: Freier Zugang