@inproceedings{1b779379230140069e47ad0775229e6d,
title = "Using uncertainty estimation to reduce false positives in liver lesion detection",
abstract = "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 a 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. ",
keywords = "Deep Learning, False Positive Detection, Lesion Detection, Uncertainty Estimation",
author = "Ishaan Bhat and Kuijf, {Hugo J.} and Veronika Cheplygina and Pluim, {Josien P.W.}",
note = "Funding Information: This work was financially supported by the project IMPACT (Intelligence based iMprovement of Personalized treatment And Clinical workflow supporT) in the framework of the EU research programme ITEA3 (Information Technology for European Advancement). The authors declare no conflict of interest. Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 ; Conference date: 13-04-2021 Through 16-04-2021",
year = "2021",
month = apr,
day = "13",
doi = "10.1109/ISBI48211.2021.9434119",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "663--667",
booktitle = "2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021",
address = "United States",
}