BM-BronchoLC

This work presents our procedure to build a rich bronchoscopy dataset named BM-BronchoLC from 106 lung cancer and 102 non-lung cancer patients. The dataset contains annotations for anatomical landmarks and lesions, which are conducted by senior doctors of the Bach Mai hospital, Vietnam. To validate the dataset quality, we investigate two typical AI backbone models for the image segmentation and classification task, i.e., Unet++ and ESFPNet, using both unitask and multitask settings. Preliminary results show the significant potential of the data in building AI models or real-time diagnostic accuracy. Further research will validate these promising developments and explore broader clinical applications in this evolving field.本研究详述了构建名为BM-BronchoLC的支气管镜检查数据集的流程，该数据集内容丰富详实，受试者涵盖106例肺癌患者与102例非肺癌患者。该数据集包含解剖标识与病变区域的标注信息，标注工作由越南巴克迈（Bach Mai）医院的资深医师完成。为验证该数据集的质量，我们针对图像分割与分类任务，选取Unet++与ESFPNet两种典型AI骨干模型，并分别采用单任务与多任务设置开展实验。初步实验结果表明，该数据集在构建AI模型或提升实时诊断精度方面具备显著应用潜力。后续研究将对这些颇具前景的研究成果进行验证，并在这一快速发展的领域中探索更广泛的临床应用场景。

搜集汇总

背景与挑战

背景概述

BM-BronchoLC是一个包含208例患者（106例肺癌和102例非肺癌）支气管镜图像的数据集，带有解剖标志和病变的专业医学标注，已成功用于验证AI模型在医学图像分析中的性能。