Boundary-guided feature integration network with hierarchical transformer for medical image segmentation

F Wang, B Wang - Multimedia Tools and Applications, 2024 - Springer
A variety of convolutional neural network (CNN) based methods for medical image
segmentation have achieved outstanding performance, however, inherently suffered from a …

[PDF][PDF] DCFNet: An Effective Dual-Branch Cross-Attention Fusion Network for Medical Image Segmentation.

C Zhu, R Zhang, Y Xiao, B Zou, X Chai… - … in Engineering & …, 2024 - cdn.techscience.cn
Automatic segmentation of medical images provides a reliable scientific basis for disease
diagnosis and analysis. Notably, most existing methods that combine the strengths of …

Hybrid-scale contextual fusion network for medical image segmentation

H Bao, Y Zhu, Q Li - Computers in Biology and Medicine, 2023 - Elsevier
Medical image segmentation result is an essential reference for disease diagnosis.
Recently, with the development and application of convolutional neural networks, medical …

CASF-Net: Cross-attention and cross-scale fusion network for medical image segmentation

J Zheng, H Liu, Y Feng, J Xu, L Zhao - Computer Methods and Programs in …, 2023 - Elsevier
Background: Automatic segmentation of medical images has progressed greatly owing to
the development of convolutional neural networks (CNNs). However, there are two …

[PDF][PDF] MLSE-Net: Multi-level Semantic Enriched Network for Medical Image Segmentation.

D Gai, H Luo, J He, P Su, Z Huang… - KSII Transactions on …, 2023 - koreascience.kr
Medical image segmentation techniques based on convolution neural networks indulge in
feature extraction triggering redundancy of parameters and unsatisfactory target localization …

CRFNet: A Medical Image Segmentation Method Using the Cross Attention Mechanism and Refined Feature Fusion Strategy

C Ma, S Tian, L Yu - Chinese Conference on Pattern Recognition and …, 2024 - Springer
Accurate medical image segmentation is essential for physicians to obtain high-quality
diagnostic results. While recent hybrid Transformer and CNN based methods have …

FI‐Net: Rethinking Feature Interactions for Medical Image Segmentation

Y Ding, J Liu, Y He, J Huang, H Liang… - Advanced Intelligent …, 2024 - Wiley Online Library
To solve the problems of existing hybrid networks based on convolutional neural networks
(CNN) and Transformers, we propose a new encoder–decoder network FI‐Net based on …

ConvTransSeg: A multi-resolution convolution-transformer network for medical image segmentation

Z Gong, AP French, G Qiu, X Chen - arXiv preprint arXiv:2210.07072, 2022 - arxiv.org
Convolutional neural networks (CNNs) achieved the state-of-the-art performance in medical
image segmentation due to their ability to extract highly complex feature representations …

DECTNet: Dual Encoder Network combined convolution and Transformer architecture for medical image segmentation

B Li, Y Xu, Y Wang, B Zhang - Plos one, 2024 - journals.plos.org
Automatic and accurate segmentation of medical images plays an essential role in disease
diagnosis and treatment planning. Convolution neural networks have achieved remarkable …

FCT-Net: Efficient Bridge Fusion Incorporating CNN-Transformer Network for Medical Image Segmentation

B Zhou, X Dong, X Zhao, C Li, Z Jin… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The hybrid architecture of CNNs and Transformers has gained popularity in medical image
segmentation. However, in this hybrid architecture, the semantic gaps between multi-scale …