Acquisition of a standard section is a prerequisite for ultrasound diagnosis. For
a long time, there has been a lack of clear definitions of standard liver views because
of physician experience. The accurate automated scanning of standard liver sections,
however, remains one of ultrasonography medicine's most important issues. In this
article, we enrich and expand the classification criteria of liver ultrasound standard
sections from clinical practice and propose an Ultra-Attention structured perception
strategy to automate the recognition of these sections. Inspired by the attention
mechanism in natural language processing, the standard liver ultrasound views will
participate in the global attention algorithm as modular local images in computer
vision of ultrasound images, which will significantly amplify small features that
would otherwise go unnoticed. In addition to using the dropout mechanism, we also
use a Part-Transfer Learning training approach to fine-tune the model's rate of convergence
to increase its robustness. The proposed Ultra-Attention model outperforms various
traditional convolutional neural network-based techniques, achieving the best known
performance in the field with a classification accuracy of 93.2%. As part of the feature
extraction procedure, we also illustrate and compare the convolutional structure and
the Ultra-Attention approach. This analysis provides a reasonable view for future
research on local modular feature capture in ultrasound images. By developing a standard
scan guideline for liver ultrasound-based illness diagnosis, this work will advance
the research on automated disease diagnosis that is directed by standard sections
of liver ultrasound.
Keywords
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Article info
Publication history
Published online: January 20, 2023
Accepted:
December 22,
2022
Received in revised form:
November 12,
2022
Received:
September 7,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 World Federation for Ultrasound in Medicine & Biology. All rights reserved.
