Deep fusion of shifted MLP and CNN for medical image segmentation

Chengyu Yuan, Hao Xiong*, Guoqing Shangguan, Hualei Shen*, Dong Liu, Haojie Zhang, Zhonghua Liu, Kun Qian, Bin Hu, Björn W. Schuller, Yoshiharu Yamamoto, Shlomo Berkovsky

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

Medical image segmentation is an important task in modern analysis of medical images. Current methods tend to extract either local features with convolutions or global features with Transformers. However, few of them are able to effectively fuse global and local features to facilitate segmentation. In this work, we propose a novel hybrid network that involves three main branches: the Multi-Layer Perception (MLP) branch, the Convolutional Neural Network (CNN) branch, and a Fusion branch. The MLP and CNN branches aim to learn global and local features, respectively. To fuse these, the fusion branch introduces a novel hierarchical fusion that performs multi-layered fusions that generate high-level representations to enhance segmentation. Our evaluation with two datasets shows strong performance of the proposed method compared to state-of-the-art baselines.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024
Subtitle of host publicationProceedings
Place of PublicationKorea
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1676-1680
Number of pages5
ISBN (Electronic)9798350344851
DOIs
Publication statusPublished - 2024
Event49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Seoul, Korea, Republic of
Duration: 14 Apr 202419 Apr 2024

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024
Country/TerritoryKorea, Republic of
CitySeoul
Period14/04/2419/04/24

Keywords

  • CNN
  • hierarchical fusion
  • Medical image segmentation
  • MLP

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