@inproceedings{1be189a316db4285a37f693c9c6808a1,
title = "Gp-Unet: Lesion detection from weak labels with a 3D regression network",
abstract = "We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count. When testing on unseen images, we first run the network to estimate the number of lesions. Then we remove the global pooling layer to compute localization maps of the size of the input image. We evaluate the proposed network on the detection of enlarged perivascular spaces in the basal ganglia in MRI. Our method achieves a sensitivity of 62% with on average 1.5 false positives per image. Compared with four other approaches based on intensity thresholding, saliency and class maps, our method has a 20% higher sensitivity.",
author = "Florian Dubost and Gerda Bortsova and Hieab Adams and Arfan Ikram and Niessen, {Wiro J.} and Meike Vernooij and {De Bruijne}, Marleen",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 ; Conference date: 11-09-2017 Through 13-09-2017",
year = "2017",
doi = "10.1007/978-3-319-66179-7_25",
language = "English",
isbn = "9783319661780",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "214--221",
editor = "Lena Maier-Hein and Alfred Franz and Pierre Jannin and Simon Duchesne and Maxime Descoteaux and Collins, {D. Louis}",
booktitle = "Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings",
}