Standard
GP-Unet : Lesion detection from weak labels with a 3D regression network. / Dubost, Florian; Bortsova, Gerda; Adams, Hieab; Ikram, Arfan; Niessen, Wiro J.; Vernooij, Meike; de Bruijne, Marleen.
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III. ed. / Maxime Descoteaux; Lena Maier-Hein; Alfred Franz; Pierre Jannin; D. Louis Collins; Simon Duschesne. Springer, 2017. p. 214-221 (Lecture notes in computer science, Vol. 10435).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Dubost, F, Bortsova, G, Adams, H, Ikram, A, Niessen, WJ, Vernooij, M
& de Bruijne, M 2017,
GP-Unet: Lesion detection from weak labels with a 3D regression network. in M Descoteaux, L Maier-Hein, A Franz, P Jannin, DL Collins & S Duschesne (eds),
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III. Springer, Lecture notes in computer science, vol. 10435, pp. 214-221, 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec City, Canada,
11/09/2017.
https://doi.org/10.1007/978-3-319-66179-7_25APA
Dubost, F., Bortsova, G., Adams, H., Ikram, A., Niessen, W. J., Vernooij, M.
, & de Bruijne, M. (2017).
GP-Unet: Lesion detection from weak labels with a 3D regression network. In M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. L. Collins, & S. Duschesne (Eds.),
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III (pp. 214-221). Springer. Lecture notes in computer science Vol. 10435
https://doi.org/10.1007/978-3-319-66179-7_25Vancouver
Dubost F, Bortsova G, Adams H, Ikram A, Niessen WJ, Vernooij M et al.
GP-Unet: Lesion detection from weak labels with a 3D regression network. In Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins DL, Duschesne S, editors, Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III. Springer. 2017. p. 214-221. (Lecture notes in computer science, Vol. 10435).
https://doi.org/10.1007/978-3-319-66179-7_25Author
Dubost, Florian ; Bortsova, Gerda ; Adams, Hieab ; Ikram, Arfan ; Niessen, Wiro J. ; Vernooij, Meike ; de Bruijne, Marleen. / GP-Unet : Lesion detection from weak labels with a 3D regression network. Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III. editor / Maxime Descoteaux ; Lena Maier-Hein ; Alfred Franz ; Pierre Jannin ; D. Louis Collins ; Simon Duschesne. Springer, 2017. pp. 214-221 (Lecture notes in computer science, Vol. 10435).
Bibtex
@inproceedings{4b619c6fadb0473e98923e75c52684c4,
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",
year = "2017",
doi = "10.1007/978-3-319-66179-7_25",
language = "English",
isbn = "978-3-319-66178-0",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "214--221",
editor = "Maxime Descoteaux and Lena Maier-Hein and Alfred Franz and Pierre Jannin and Collins, {D. Louis} and Simon Duschesne",
booktitle = "Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017",
address = "Switzerland",
note = "null ; Conference date: 11-09-2017 Through 13-09-2017",
}
RIS
TY - GEN
T1 - GP-Unet
AU - Dubost, Florian
AU - Bortsova, Gerda
AU - Adams, Hieab
AU - Ikram, Arfan
AU - Niessen, Wiro J.
AU - Vernooij, Meike
AU - de Bruijne, Marleen
N1 - Conference code: 20
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85029548373&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66179-7_25
DO - 10.1007/978-3-319-66179-7_25
M3 - Article in proceedings
AN - SCOPUS:85029548373
SN - 978-3-319-66178-0
T3 - Lecture notes in computer science
SP - 214
EP - 221
BT - Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
A2 - Descoteaux, Maxime
A2 - Maier-Hein, Lena
A2 - Franz, Alfred
A2 - Jannin, Pierre
A2 - Collins, D. Louis
A2 - Duschesne, Simon
PB - Springer
Y2 - 11 September 2017 through 13 September 2017
ER -
