Studying the Effects of Self-Attention for Medical Image Analysis
- URL: http://arxiv.org/abs/2109.01486v1
- Date: Thu, 2 Sep 2021 07:07:16 GMT
- Title: Studying the Effects of Self-Attention for Medical Image Analysis
- Authors: Adrit Rao, Jongchan Park, Sanghyun Woo, Joon-Young Lee, Oliver Aalami
- Abstract summary: We compare various state-of-the-art self-attention mechanisms across multiple medical image analysis tasks.
We aim to provide a deeper understanding of the effects of self-attention in medical computer vision tasks.
- Score: 42.12044020360494
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: When the trained physician interprets medical images, they understand the
clinical importance of visual features. By applying cognitive attention, they
apply greater focus onto clinically relevant regions while disregarding
unnecessary features. The use of computer vision to automate the classification
of medical images is widely studied. However, the standard convolutional neural
network (CNN) does not necessarily employ subconscious feature relevancy
evaluation techniques similar to the trained medical specialist and evaluates
features more generally. Self-attention mechanisms enable CNNs to focus more on
semantically important regions or aggregated relevant context with long-range
dependencies. By using attention, medical image analysis systems can
potentially become more robust by focusing on more important clinical feature
regions. In this paper, we provide a comprehensive comparison of various
state-of-the-art self-attention mechanisms across multiple medical image
analysis tasks. Through both quantitative and qualitative evaluations along
with a clinical user-centric survey study, we aim to provide a deeper
understanding of the effects of self-attention in medical computer vision
tasks.
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