  Despite the successes of deep learning techniques at detecting objects in
medical images, false positive detections occur which may hinder an accurate
diagnosis. We propose a technique to reduce false positive detections made by a
neural network using an SVM classifier trained with features derived from the
uncertainty map of the neural network prediction. We demonstrate the
effectiveness of this method for the detection of liver lesions on a dataset of
abdominal MR images. We find that the use of a dropout rate of 0.5 produces the
least number of false positives in the neural network predictions and the
trained classifier filters out approximately 90% of these false positives
detections in the test-set.
