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Discrete Wavelet Coefficient-based Embeddable Branch for Ultrasound Breast Masses Classification

Published: 07 June 2023 Publication History

Abstract

The progress of computer-aid-diagnosis system for ultrasound breast lesions reaches tremendous success in the past few years. However, conventional deep learning-based strategies in recent developments still have challenges particularly in characterizing tumor domain in ultrasound images due to the heterogeneous and complex variations of lesions along with similar intensity exhibited in target object. To address this, this work proposes a discrete wavelet coefficient-based embeddable branch that allows to additionally propagate geometrical features of tumors in an end-to-end trainable fashion. To be elaborate, such branch priorly enforce the wavelet pooling operation to select a certain coefficient to further collect gradient information of target domain. Further, the current work also investigates two different preprocessing strategies in which the internal and external gradients of lesion areas can be emphasized within the transformation. Thus, we examine the effects of the proposed method based on different preprocessing scenarios. To verify the usefulness, GradCam projection, and the cross-validation demonstrate the connection of the proposed branch encourages the importance of target features, thus boosting the overall discrimination between lesion groups. Lastly, the proposed branch can be easily incorporated with existing deep learning-based architectures.

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  1. Discrete Wavelet Coefficient-based Embeddable Branch for Ultrasound Breast Masses Classification

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        cover image ACM Conferences
        SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
        March 2023
        1932 pages
        ISBN:9781450395175
        DOI:10.1145/3555776
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Published: 07 June 2023

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        Author Tags

        1. discrete wavelet transformation
        2. deep learning
        3. wavelet coefficient
        4. ultrasound breast lesion classification

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