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Found 6 papers, 4 papers with code
TEC-Net: Vision Transformer Embrace Convolutional Neural Networks for Medical Image Segmentation
The hybrid architecture of convolution neural networks (CNN) and Transformer has been the most popular method for medical image segmentation.
Lightweight Structure-aware Transformer Network for VHR Remote Sensing Image Change Detection
Popular Transformer networks have been successfully applied to remote sensing (RS) image change detection (CD) identifications and achieve better results than most convolutional neural networks (CNNs), but they still suffer from two main problems.
Ultralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images
First, the existing multiscale feature fusion methods often use redundant feature extraction and fusion strategies, which often lead to high computational costs and memory usage.
Ranked #2 on
Change Detection
on DSIFN-CD
Building change detection for remote sensing images
Change Detection
+1
Medical Image Segmentation Using Deep Learning: A Survey
Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi-level structure from coarse to fine.
Adaptive Morphological Reconstruction for Seeded Image Segmentation
However, MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed.
Diffusion map for clustering fMRI spatial maps extracted by independent component analysis
In this research, we used the recently developed diffusion map for dimensionality reduction in conjunction with spectral clustering.
