Generating large quantities of quality labeled data in medical imaging is
very time consuming and expensive. The performance of supervised algorithms for
various tasks on imaging has improved drastically over the years, however the
availability of data to train these algorithms have become one of the main
bottlenecks for implementation. To address this, we propose a semi-supervised
learning method where pseudo-negative labels from unlabeled data are used to
further refine the performance of a pulmonary nodule detection network in chest
radiographs. After training with the proposed network, the false positive rate
was reduced to 0.1266 from 0.4864 while maintaining sensitivity at 0.89.

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