@inproceedings{2529d50cb7f342c590bef6d506ff629e,
title = "Deep fusion of shifted MLP and CNN for medical image segmentation",
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.",
keywords = "CNN, hierarchical fusion, Medical image segmentation, MLP",
author = "Chengyu Yuan and Hao Xiong and Guoqing Shangguan and Hualei Shen and Dong Liu and Haojie Zhang and Zhonghua Liu and Kun Qian and Bin Hu and Schuller, {Bj{\"o}rn W.} and Yoshiharu Yamamoto and Shlomo Berkovsky",
year = "2024",
doi = "10.1109/ICASSP48485.2024.10446716",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "1676--1680",
booktitle = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024",
address = "United States",
note = "49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 ; Conference date: 14-04-2024 Through 19-04-2024",
}