Recalibrating 3D ConvNets with Project & Excite
- URL: http://arxiv.org/abs/2002.10994v1
- Date: Tue, 25 Feb 2020 16:07:17 GMT
- Title: Recalibrating 3D ConvNets with Project & Excite
- Authors: Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua, Christian
Wachinger
- Abstract summary: Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging.
We extend existing 2D recalibration methods to 3D and propose a generic compress-process-recalibrate pipeline for easy comparison.
We demonstrate that PE modules can be easily integrated into 3D F-CNNs, boosting performance up to 0.3 in Dice Score and outperforming 3D extensions of other recalibration blocks.
- Score: 6.11737116137921
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art
performance for segmentation tasks in computer vision and medical imaging.
Recently, computational blocks termed squeeze and excitation (SE) have been
introduced to recalibrate F-CNN feature maps both channel- and spatial-wise,
boosting segmentation performance while only minimally increasing the model
complexity. So far, the development of SE blocks has focused on 2D
architectures. For volumetric medical images, however, 3D F-CNNs are a natural
choice. In this article, we extend existing 2D recalibration methods to 3D and
propose a generic compress-process-recalibrate pipeline for easy comparison of
such blocks. We further introduce Project & Excite (PE) modules, customized for
3D networks. In contrast to existing modules, Project \& Excite does not
perform global average pooling but compresses feature maps along different
spatial dimensions of the tensor separately to retain more spatial information
that is subsequently used in the excitation step. We evaluate the modules on
two challenging tasks, whole-brain segmentation of MRI scans and whole-body
segmentation of CT scans. We demonstrate that PE modules can be easily
integrated into 3D F-CNNs, boosting performance up to 0.3 in Dice Score and
outperforming 3D extensions of other recalibration blocks, while only
marginally increasing the model complexity. Our code is publicly available on
https://github.com/ai-med/squeeze_and_excitation .
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