GP-Unet: Lesion detection from weak labels with a 3D regression network

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  • Florian Dubost
  • Gerda Bortsova
  • Hieab Adams
  • Arfan Ikram
  • Wiro J. Niessen
  • Meike Vernooij
  • de Bruijne, Marleen

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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention − MICCAI 2017 : 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III
EditorsMaxime Descoteaux, Lena Maier-Hein, Alfred Franz, Pierre Jannin, D. Louis Collins, Simon Duschesne
Number of pages8
Publication date2017
ISBN (Print)978-3-319-66178-0
ISBN (Electronic)978-3-319-66179-7
Publication statusPublished - 2017
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention - Quebec City, Canada
Duration: 11 Sep 201713 Sep 2017
Conference number: 20


Conference20th International Conference on Medical Image Computing and Computer-Assisted Intervention
ByQuebec City
SeriesLecture notes in computer science


ID: 184141962