Summary, Survey & Review |
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Deep Learning |
Nature |
2015 |
A Survey on Deep Learning in Medical Image Analysis |
MedIA |
2017 |
Bag of Tricks for Image Classification with Convolutional Neural Networks |
CVPR |
2019 |
Deep Learning in Medical Ultrasound Analysis: A Review |
Engineering |
2019 |
Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey |
IEEE TPAMI |
2020 |
A Comprehensive Survey on Graph Neural Networks |
IEEE TNNLS |
2020 |
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges |
arXiv |
2021 |
Curriculum Learning: A Survey |
arXiv |
2021 |
Loss Odyssey in Medical Image Segmentation |
MedIA |
2021 |
Basic Technology |
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting |
JMLR |
2014 |
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift |
ICML |
2015 |
Instance Normalization: The Missing Ingredient for Fast Stylization |
arXiv |
2016 |
Layer Normalization |
NeurIPS |
2016 |
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization |
ICCV |
2017 |
Group Normalization |
ECCV |
2018 |
DropBlock: A Regularization Method for Convolutional Networks |
NeurIPS |
2018 |
Classification & Recognition |
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ImageNet Classification with Deep Convolutional Neural Networks |
NeurIPS |
2012 |
Very Deep Convolutional Networks for Large-Scale Image Recognition |
ICLR |
2015 |
Going Deeper with Convolutions |
CVPR |
2015 |
Deep Residual Learning for Image Recognition |
CVPR |
2016 |
Identity Mappings in Deep Residual Networks |
ECCV |
2016 |
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning |
AAAI |
2017 |
Densely Connected Convolutional Networks |
CVPR |
2017 |
Aggregated Residual Transformations for Deep Neural Networks |
CVPR |
2017 |
Dynamic Routing Between Capsules |
NeurIPS |
2017 |
Squeeze-and-Excitation Networks |
CVPR |
2018 |
Non-local Neural Networks |
CVPR |
2018 |
CBAM: Convolutional Block Attention Module |
ECCV |
2018 |
MixConv: Mixed Depthwise Convolutional Kernels |
BMVC |
2019 |
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution |
ICCV |
2019 |
Res2Net: A New Multi-scale Backbone Architecture |
IEEE TPAMI |
2019 |
Focus Longer to See Better: Recursively Refined Attention for Fine-grained Image Classification |
CVPR |
2020 |
RepVGG: Making VGG-style ConvNets Great Again |
arXiv |
2021 |
Segmentation |
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Fully Convolutional Networks for Semantic Segmentation |
CVPR |
2015 |
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding |
arXiv |
2015 |
U-Net: Convolutional Networks for Biomedical Image Segmentation |
MICCAI |
2015 |
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation |
MICCAI |
2016 |
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation |
3DV |
2016 |
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation |
IEEE TPAMI |
2017 |
UNet++: A Nested U-Net Architecture for Medical Image Segmentation |
DLMIA |
2018 |
VoxResNet: Deep Voxelwise Residual Networks for Brain Segmentation from 3D MR Images |
NeuroImage |
2018 |
Attention U-Net: Learning Where to Look for the Pancreas |
MIDL |
2018 |
ICNet for Real-Time Semantic Segmentation on High-Resolution Images |
ECCV |
2018 |
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes |
IEEE TPAMI |
2018 |
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs |
IEEE TPAMI |
2018 |
Recalibrating Fully Convolutional Networks with Spatial and Channel “Squeeze and Excitation” Blocks |
IEEE TMI |
2018 |
Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images |
ICCV |
2019 |
Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation |
MICCAI |
2019 |
Dual Attention Network for Scene Segmentation |
CVPR |
2019 |
Capsules for Biomedical Image Segmentation |
MedIA |
2020 |
LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation |
CVPR |
2020 |
Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation |
CVPR |
2020 |
PointRend: Image Segmentation as Rendering |
CVPR |
2020 |
Detection |
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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation |
CVPR |
2014 |
Fast R-CNN |
ICCV |
2015 |
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks |
NeurIPS |
2015 |
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition |
IEEE TPAMI |
2015 |
SSD: Single Shot MultiBox Detector |
ECCV |
2016 |
Focal Loss for Dense Object Detection |
ICCV |
2017 |
Feature Pyramid Networks for Object Detection |
CVPR |
2017 |
Mask R-CNN |
ICCV |
2017 |
Registration |
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An Unsupervised Learning Model for Deformable Medical Image Registration |
CVPR |
2018 |
Weakly-supervised Convolutional Neural Networks for Multi-modal Image Registration |
MedIA |
2018 |
Adversarial Learning for Mono- or Multi-modal Registration |
MedIA |
2019 |
Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration |
MICCAI |
2020 |
Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation |
CVPR |
2020 |
Reinforcement Learning |
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Human-level Control through Deep Reinforcement Learning |
Nature |
2015 |
Deep Reinforcement Learning with Double Q-learning |
AAAI |
2016 |
Dueling Network Architectures for Deep Reinforcement Learning |
ICML |
2016 |
Prioritized Experience Replay |
ICLR |
2016 |
Learning to paint with model-based deep reinforcement learning |
ICCV |
2019 |
Evaluating Reinforcement Learning Agents for Anatomical Landmark Detection |
MedIA |
2019 |
Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans |
IEEE TPAMI |
2019 |
Self-supervised Learning |
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Robust Learning Through Cross-Task Consistency |
CVPR |
2020 |
Momentum Contrast for Unsupervised Visual Representation Learning |
CVPR |
2020 |
A Simple Framework for Contrastive Learning of Visual Representations |
arXiv |
2020 |
Geometric Learning |
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Variational Graph Auto-Encoders |
arXiv |
2016 |
A Point Set Generation Network for 3D Object Reconstruction from a Single Image |
CVPR |
2017 |
Generating 3D faces using Convolutional Mesh Autoencoders |
ECCV |
2018 |
3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata |
MICCAI |
2019 |
Pixel2Mesh: 3D Mesh Model Generation via Image Guided Deformation |
IEEE TPAMI |
2020 |
Learning to Infer Semantic Parameters for 3D Shape Editing |
arXiv |
2020 |
Learning Part Generation and Assembly for Structure-Aware Shape Synthesis |
AAAI |
2020 |
Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data |
MICCAI |
2020 |
Joint Learning of 3D Shape Retrieval and Deformation |
arXiv |
2021 |
NEMO : Neural Mesh Models of Contrastive Feature for Robust 3D Pose Estimation |
ICLR |
2021 |
Generative, Super-resolution & Simulation Model |
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Auto-Encoding Variational Bayes |
arXiv |
2013 |
MRISIMUL: A GPU-Based Parallel Approach to MRI Simulations |
IEEE TMI |
2017 |
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks |
ICCV |
2017 |
Image-to-Image Translation with Conditional Adversarial Nets |
CVPR |
2017 |
Learning to Simulate Complex Scenes |
ACM MM |
2019 |
Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data |
ICML |
2019 |
Learning to Simulate |
ICLR |
2019 |
Learning Conditional Deformable Templates with Convolutional Networks |
NeurIPS |
2019 |
Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data |
ICML |
2019 |
Parameter-Free Style Projection for Arbitrary Style Transfer |
arXiv |
2020 |
Best-Buddy GANs for Highly Detailed Image Super-Resolution |
arXiv |
2021 |
View Planning |
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SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound |
IEEE TMI |
2017 |
Ultrasound Standard Plane Detection Using a Composite Neural Network Framework |
IEEE TCs |
2017 |
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network |
MICCAI |
2018 |
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents |
MICCAI |
2018 |
Ultrasound Video Summarization using Deep Reinforcement Learning |
MICCAI |
2020 |