Using uncertainty estimation to reduce false positives in liver lesion detection

Autor:	Bhat, Ishaan, Kuijf, Hugo J., Cheplygina, Veronika, Pluim, Josien P. W.
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at IEEE ISBI 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2101.04386 Zobrazit plný text záznamu View this record from Arxiv