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.

本文全面比较了各种最先进的自注意机制在多个医学图像分析任务中的效果，并通过定量、定性和临床用户调查，旨在深入了解自注意在医学计算机视觉任务中的影响。