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2018 | OriginalPaper | Buchkapitel

Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks

verfasst von : Abhijit Guha Roy, Nassir Navab, Christian Wachinger

Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Verlag: Springer International Publishing

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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.

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Metadaten
Titel
Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks
verfasst von
Abhijit Guha Roy
Nassir Navab
Christian Wachinger
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-00928-1_48