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2020 | OriginalPaper | Chapter

Convolutional Attention on Images for Locating Macular Edema

Authors : Maximilian Bryan, Gerhard Heyer, Nathanael Philipp, Matus Rehak, Peter Wiedemann

Published in: Medical Image Understanding and Analysis

Publisher: Springer International Publishing

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Abstract

Neural networks have become a standard for classifying images. However, by their very nature, their internal data representation remains opaque. To solve this dilemma, attention mechanisms have recently been introduced. They help to highlight regions in input data that have been used for a network’s classification decision. This article presents two attention architectures for the classification of medical images. Firstly, we are explaining a simple architecture which creates one attention map that is used for all classes. Secondly, we introduce an architecture that creates an attention map for each class. This is done by creating two U-nets - one for attention and one for classification - and then multiplying these two maps together. We show that our architectures well meet the baseline of standard convolutional classifications while at the same time increasing their explainability.

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Title: Convolutional Attention on Images for Locating Macular Edema
Authors: Maximilian Bryan
Gerhard Heyer
Nathanael Philipp
Matus Rehak
Peter Wiedemann
Publisher: Springer International Publishing
Book: Medical Image Understanding and Analysis
Print ISBN: 978-3-030-39342-7

Electronic ISBN: 978-3-030-39343-4

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-39343-4_33

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