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2018 | OriginalPaper | Chapter

Autofocus Layer for Semantic Segmentation

Authors : Yao Qin, Konstantinos Kamnitsas, Siddharth Ancha, Jay Nanavati, Garrison Cottrell, Antonio Criminisi, Aditya Nori

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

Publisher: Springer International Publishing

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Abstract

We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing. Autofocus layers adaptively change the size of the effective receptive field based on the processed context to generate more powerful features. This is achieved by parallelising multiple convolutional layers with different dilation rates, combined by an attention mechanism that learns to focus on the optimal scales driven by context. By sharing the weights of the parallel convolutions we make the network scale-invariant, with only a modest increase in the number of parameters. The proposed autofocus layer can be easily integrated into existing networks to improve a model’s representational power. We evaluate our mod els on the challenging tasks of multi-organ segmentation in pelvic CT and brain tumor segmentation in MRI and achieve very promising performance.

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previous chapter Semi-supervised Learning for Segmentation Under Semantic Constraint

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Title: Autofocus Layer for Semantic Segmentation
Authors: Yao Qin
Konstantinos Kamnitsas
Siddharth Ancha
Jay Nanavati
Garrison Cottrell
Antonio Criminisi
Aditya Nori
Publisher: Springer International Publishing
Book: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Print ISBN: 978-3-030-00930-4

Electronic ISBN: 978-3-030-00931-1

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00931-1_69

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