Image Registration

  1. [ ] Pairwise domain adaptation module for CNN-based 2-D/3-D registration \cite{Zheng_2018}
  2. [ ] An Unsupervised Learning Model for Deformable Medical Image Registration \cite{balakrishnan2018unsupervised} - from MIT

Generative Models

  1. [ ] Spatial PixelCNN: Generating Images from Patches \cite{1712.00714}
  2. [ ] High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs \cite{wang2017high}
  3. [ ] Data Augmentation Generative Adversarial Networks \cite{antoniou2017data}
  4. [ ] Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks \cite{Zhu_2017}

Image Reconstruction and Super-Resolution

  1. [ ] MR image reconstruction using the learned data distribution as prior \cite{1711.11386}
  2. [ ] Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks \cite{hu2017intraoperative}
  3. [ ] Volumetric reconstruction from printed films: Enabling 30 year longitudinal analysis in MR neuroimaging \cite{Ebner_2018}
  4. [ ] DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction\cite{Yang_2018}

Unsupervised Deep Learning

  1. [ ] Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training \cite{mahmood2017unsupervised}

Self-Supervised Deep Learning

  1. [ ] Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge \cite{Zeng_2017}

Deep Learning on Graphs

  1. [ ] BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment \cite{Kawahara2017}

Reinforcement Learning

  1. [ ] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm \cite{1712.01815}
  2. [ ] Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans \cite{Ghesu_2017}

Deep Networks Visualisation and Interpretation

  1. [ ] The (Un)reliability of saliency methods \cite{kindermans2017reliability}
  2. [ ] DeepXplore: Automated Whitebox Testing of Deep Learning Systems \cite{pei2017deepxplore}
  3. [ ] Feature Visualisation \cite{olah2017feature}  (new blog from google with fancy images)
  4. [ ] Visualizing the loss landscape of neural nets\cite{li2017visualizing}
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Notes

  1. [Bernhard] An online book about interpretable machine learning - mentioned on 07/12/2017: https://christophm.github.io/interpretable-ml-book/

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Previous Papers

2018

  1. [x] Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images \cite{zhang2017deep}
  2. [x] Dynamic Routing Between Capsules \cite{Sabour2017}Reddit
  3. [x] Attention Is All You Need \cite{vaswani2017attention}
  4. [x] Unsupervised End-to-end Learning for Deformable Medical Image Registration \cite{shan2017unsupervised}
  5. [x] Mask R-CNN \cite{he2017mask}
  6. [x] A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs. \cite{George_2017}
  7. [x] ST-GAN: Spatial Transformer Generative Adversarial Networksfor Image Compositing  \cite{1803.01837}github 
  8. [x] Nonparametric Variational Auto-encoders for Hierarchical Representation Learning \cite{Goyal_2017}
  9. [X] Multi-task Self-Supervised Visual Learning \cite{Doersch_2017}
  10. [X] Born Again Neural Networks - distilling a teacher model to a student model with an identical architecture, the student outperforms the teacher (using a dense net)\cite{furlanelloborn}

2017

  1. [x] CNN Fixations: An unraveling approach to visualize the discriminative image regions \cite{Mopuri2017}
  2. [x] Visualizing Deep Neural Network Decisions: Prediction Difference Analysis \cite{Zintgraf2017}
  3. [x] Soft Proposal Networks for Weakly Supervised Object Localization \cite{Zhu2017}
  4. [x] Self-supervised Learning for Spinal MRIs \cite{Jamaludin_2017}
  5. [x] Visual Feature Attribution using Wasserstein GANs \cite{1711.08998}
  6. [x] Non-local Neural Networks \cite{1711.07971} - facebookAI - Achieves best results in video classification, object segmentation and pose estimation -  Reddit 
  7. [x] Distilling a Neural Network Into a Soft Decision Tree \cite{1711.09784} - describes a way of using a trained neural net to create a type of soft decision tree that generalizes better than one learned directly from the training data - Reddit

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