False Positive Reduction by Actively Mining Negative Samples for Pulmonary Nodule Detection in Chest Radiographs

  • Sep. 2017
  • by Jeong Rye Kim et. al.

While CADe(Computer aided detection) systems can achieve high sensitivity, their relatively low specificity has limited its implementation in the clinical setting. One of the major limiting factors for false positive reduction is the lack of good quality labeled data(with lesion labels). Our approach to solving this problem was utilizing unlabeled data (with unknown lesion and class labels), which tends to be more readily available. The goal of this study is to develop a semi-supervised learning method, that allows us to find pseudo-negative labeled data from unlabeled data and use this to improve the specificity of the detection task. We will then compare this to the false positive reduction achieved using clinically verified negative data, which is the theoretical optimum within our model and data setting.


Jeong Rye Kim, Woo Hyun Shim, Hee Mang Yoon, Sang Hyup Hong, Jin Seong Lee, Young Ah Cho and Sangki Kim

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