TBX11K Tuberculosis Classification and Detection Challenge
Yun Liu*, Yu-Huan Wu*, Yunfeng Ban, Huifang Wang, Ming-Ming Cheng

Type: Dataset

title= {TBX11K Tuberculosis Classification and Detection Challenge},
keywords= {},
author= {Yun Liu*, Yu-Huan Wu*, Yunfeng Ban, Huifang Wang, Ming-Ming Cheng},
abstract= {As a serious infectious disease, tuberculosis (TB) is one of the major threats to human health worldwide, leading to millions of death every year. Although early diagnosis and treatment can greatly improve the chances of survival, it remains a major challenge, especially in developing countries. Computer-aided tuberculosis diagnosis (CTD) is a promising choice for TB diagnosis due to the great successes of deep learning. However, when it comes to TB diagnosis, the lack of training data has hampered the progress of CTD. To solve this problem, we establish a large-scale TB dataset, namely Tuberculosis X-ray (TBX11K) dataset. This dataset contains 11200 X-ray images with corresponding bounding box annotations for TB areas, while the existing largest public TB dataset only has 662 X-ray images with corresponding image-level annotations. The proposed dataset enables the training of sophisticated detectors for high-quality CTD.

Rethinking Computer-Aided Tuberculosis Diagnosis, Yun Liu*, Yu-Huan Wu*, Yunfeng Ban, Huifang Wang, Ming-Ming Cheng, IEEE CVPR, 2020.

terms= {},
license= {https://creativecommons.org/licenses/by/4.0/},
superseded= {},
url= {https://mmcheng.net/tb/}

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