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.
To adapt to the practical demand, this challenge aims at simultaneous tuberculosis (TB) X-ray classification and TB area detection in a single system (e.g., a convolutional neural network). The X-ray classification focuses on classifying each test X-ray into one of three categories, including Healthy, Sick but Non-TB, and TB (the super-category of active TB and latent TB). We adopt six metrics to evaluate the X-ray classification results:
For the evaluation of TB detection, we adopt the average precision of bounding box (AP) proposed by the COCO dataset challenge. The default AP refers to the AP averaged over IoU (intersection-over-union) thresholds of [0.5 : 0.05 : 0.95]. AP50 refers to AP at the threshold of 0.5. Similarly, AP75 refers to AP at the threshold of 0.75. In order to observe the detection of each TB type, we report the evaluation results for active TB and latent TB separately. Here, the uncertain TB X-rays are ignored (uncertain TB X-rays only exist in the test set). We also report category-agnostic TB detection results, where the TB categories are ignored, to describe the detection for all TB areas. Here, the uncertain TB X-rays are included. Note that there exist both TB and non-TB X-rays in the test set. In the ideal case, one model should not predict TB areas for a non-TB X-ray. In other words, the evaluation program will penalize the false positives in a non-TB X-ray.
This dataset belongs to the Media Computing Lab at Nankai University and is licensed under a Creative Commons Attribution 4.0 License.
Start: Aug. 3, 2020, midnight
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