Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks
Roy AG, Navab N, Wachinger C (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 11070 LNCS
Pages Range: 421-429
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Granada, ESP
ISBN: 9783030009274
DOI: 10.1007/978-3-030-00928-1_48
Abstract
Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image segmentation for a plethora of applications. Architectural innovations within F-CNNs have mainly focused on improving spatial encoding or network connectivity to aid gradient flow. In this paper, we explore an alternate direction of recalibrating the feature maps adaptively, to boost meaningful features, while suppressing weak ones. We draw inspiration from the recently proposed squeeze & excitation (SE) module for channel recalibration of feature maps for image classification. Towards this end, we introduce three variants of SE modules for image segmentation, (i) squeezing spatially and exciting channel-wise (cSE), (ii) squeezing channel-wise and exciting spatially (sSE) and (iii) concurrent spatial and channel squeeze & excitation (scSE). We effectively incorporate these SE modules within three different state-of-the-art F-CNNs (DenseNet, SD-Net, U-Net) and observe consistent improvement of performance across all architectures, while minimally effecting model complexity. Evaluations are performed on two challenging applications: whole brain segmentation on MRI scans and organ segmentation on whole body contrast enhanced CT scans.
Involved external institutions
Germany (DE)How to cite
APA:
Roy, A.G., Navab, N., & Wachinger, C. (2018). Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks. In Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger, Alejandro F. Frangi (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 421-429). Granada, ESP: Springer Verlag.
MLA:
Roy, Abhijit Guha, Nassir Navab, and Christian Wachinger. "Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks." Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger, Alejandro F. Frangi, Springer Verlag, 2018. 421-429.
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