Collaborative networks of transformers and convolutional neural networks are powerful and versatile learners for accurate 3D medical image segmentation

Deep Residual Learning for Image Recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. · 2016 · 214.9K citations · Full text

Image Classification, Deep Neural Networks, Machine Vision +14

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Ze Liu, Yutong Lin, Yue Cao et al. · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · 2021 · 27.9K citations

Swin Transformer, Convolutional Neural Network, Machine Vision +14

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2017 · 21.4K citations

Semantic Image Segmentation, Convolutional Neural Network, Scene Analysis +15

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

Nature Methods · 2020 · 7.5K citations · Full text

Convolutional Neural Network, Medical Image Segmentation, Image Analysis +14

TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Jieneng Chen, Yongyi Lu, Qihang Yu et al. · arXiv (Cornell University) · 2021 · 3.8K citations · Full text

Convolutional Neural Network, Medical Image Segmentation, Engineering +19