Using uncertainty estimation to reduce false positives in liver lesion detection
This work addresses false positives in medical imaging for liver lesion diagnosis, but it is incremental as it builds on existing deep learning methods with a specific filtering technique.
The paper tackled the problem of false positives in liver lesion detection from abdominal MR images by using an SVM classifier trained on uncertainty map features from a neural network, reducing false positives by approximately 90% in the test set.
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.