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【论文笔记】CVPR2020 Rethinking Computer-aided Tuberculosis Diagnosis

在cvpr上少见的使用medical data的paper

Contributions

  • 收集了新的很大的TB dataset:Tuberculosis X-ray (TBX11K) dataset,包括:
    11200 X-ray Images
    Image-level annotation + TB area annotation using bounding boxes
    Image-level annotations include 4 classes: healthy, active TB, latent TB, & unhealthy but non-TB

  • Reform existing object detectors to perform simultaneous image classification and TB area detection (SSD, RetinaNet, Faster-RCNN, FCOS),并定义了classification 和 detection 的 metrics。 作为dataset的baselines

Methods

  • the classification branch learns to classify X-rays into 3 classes: healthy, sick but non-TB, and TB
    evaluation metrics: accuracy, auc, sensitivity…

  • the detection branch learns to detect TBs with 3 classes: active TB, latent TB
    evaluation metrics: average precision of bounding box

Results

  • 和其他datasets的对比,比其他大很多
  • 作者对于每个baseline model都做了实验。从结果上看,Faster-RCNN 和 SSD 的表现比较突出。
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