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Generating large quantities of quality labeled data in medical imaging isvery time consuming and expensive. The performance of supervised algorithms forvarious tasks on imaging has improved drastically over the years, however theavailability of data to train these algorithms have become one of the mainbottlenecks for implementation. To address this, we propose a semi-supervisedlearning method where pseudo-negative labels from unlabeled data are used tofurther refine the performance of a pulmonary nodule detection network in chestradiographs. After training with the proposed network, the false positive ratewas reduced to 0.1266 from 0.4864 while maintaining sensitivity at 0.89.

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