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

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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. red. / Maxime Descoteaux; Lena Maier-Hein; Alfred Franz; Pierre Jannin; D. Louis Collins; Simon Duschesne. Springer, 2017. s. 214-221 (Lecture notes in computer science, Bind 10435).

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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. i M Descoteaux, L Maier-Hein, A Franz, P Jannin, DL Collins & S Duschesne (red), 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, bind 10435, s. 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_25

APA

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. I M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. L. Collins, & S. Duschesne (red.), Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III (s. 214-221). Springer. Lecture notes in computer science, Bind. 10435 https://doi.org/10.1007/978-3-319-66179-7_25

Vancouver

Dubost F, Bortsova G, Adams H, Ikram A, Niessen WJ, Vernooij M o.a. GP-Unet: Lesion detection from weak labels with a 3D regression network. I Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins DL, Duschesne S, red., 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. s. 214-221. (Lecture notes in computer science, Bind 10435). https://doi.org/10.1007/978-3-319-66179-7_25

Author

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. red. / Maxime Descoteaux ; Lena Maier-Hein ; Alfred Franz ; Pierre Jannin ; D. Louis Collins ; Simon Duschesne. Springer, 2017. s. 214-221 (Lecture notes in computer science, Bind 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",
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 -

ID: 184141962