Encoder-decoder networks
AI/Deep Learning/DL architectures composed of two paths, an encoding and a decoding one. AI/Deep Learning/Autoencoders are similar but unsupervised (reconstructions loss). U-NETs are a type of encoder-decoder AI/Deep Learning/CNNs model with skipped connections trained in a AI/Supervised Learning/Supervised learning context for image segmentation and related tasks. Very common models for semantic segmentation tasks
Resources
Code
References
- #PAPER U-Net: Convolutional Networks for Biomedical Image Segmentation (Ronneberger 2015)
- https://towardsdatascience.com/review-u-net-biomedical-image-segmentation-d02bf06ca760
- https://github.com/karolzak/keras-unet
- https://tuatini.me/practical-image-segmentation-with-unet/
- https://github.com/divamgupta/image-segmentation-keras/blob/master/keras_segmentation/models/unet.py
- diagram example
- #PAPER 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation (Cicek 2016)
- #PAPER V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation (Milletari 2016)
- #PAPER Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images, 3D UNET+Resnet (Yu 2017)
- #PAPER Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets, DenseVoxNet (Yu 2017)
- #PAPER Road Extraction by Deep Residual U-Net, ResUNET (Zhang 2017)
- #PAPER Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation (Alom 2018)
- #PAPER UNet++: A Nested U-Net Architecture for Medical Image Segmentation (Zhou 2018)
- #PAPER H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes (Li 2018)
- #PAPER Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, DeepLabv3+ (Chen 2018)
- #PAPER ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data (Diakogiannis 2019)
- #PAPER ResUNet++: An Advanced Architecture for Medical Image Segmentation (Jha 2019)
- #PAPER M-Net: U-Net with Multi-stream Feature Fusion and Multi-scale Dilated Convolutions (Fu 2019)
- #PAPER Channel-Unet: A Spatial Channel-Wise Convolutional Neural Network for Liver and Tumors Segmentation (Chen 2019)
- #PAPER Bi-Directional ConvLSTM U-Net with Densely Connected Convolutions (Azad 2019)
- #PAPER LSTM-UNET - Microscopy Cell Segmentation via Convolutional LSTM Networks (Arbelle 2019)
#CODE https://github.com/arbellea/LSTM-UNet - #PAPER USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets (Rundo 2019)
- #PAPER Evaluation of Multi-Slice Inputs to Convolutional Neural Networks for Medical Image Segmentation (Vu 2019)
- #PAPER Making a Case for 3D Convolutions for Object Segmentation in Videos (Mahadevan 2020)
- proposed a simple and fast network architecture consisting entirely of 3D convolutions that is capable of effectively learning spatio-temporal features
- used a 3D ResNet pretrained for video action classification as an encoder, and a novel decoder architecture inspired by existing 2D convolutional networks
- #PAPER nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation (Isensee 2020)
- #PAPER MCNN, Multi-resolution convolutional neural networks for inverse problems (Wang 2020)