人工智能
计算机视觉
计算机科学
图像分割
分割
融合
图像融合
模式识别(心理学)
尺度空间分割
图像(数学)
哲学
语言学
作者
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
标识
DOI:10.1109/icassp48485.2024.10446716
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI