%%%%%%%%%%%%%%%%%% CMR %%%%%%%%%%%%%%%%%%

@article{oksuz2019automatic,
  title={Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning},
  author={Oksuz, Ilkay and Ruijsink, Bram and Puyol-Ant{\'o}n, Esther and Clough, James R and Cruz, Gastao and Bustin, Aurelien and Prieto, Claudia and Botnar, Rene and Rueckert, Daniel and Schnabel, Julia A and others},
  journal={Medical image analysis},
  volume={55},
  pages={136--147},
  year={2019},
  publisher={Elsevier}
}

@article{ruijsink2020fully,
  title={Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function},
  author={Ruijsink, Bram and Puyol-Ant{\'o}n, Esther and Oksuz, Ilkay and Sinclair, Matthew and Bai, Wenjia and Schnabel, Julia A and Razavi, Reza and King, Andrew P},
  journal={Cardiovascular Imaging},
  volume={13},
  number={3},
  pages={684--695},
  year={2020},
  publisher={American College of Cardiology Foundation Washington DC}
}

@inproceedings{isensee2017automatic,
  title={Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features},
  author={Isensee, Fabian and Jaeger, Paul F and Full, Peter M and Wolf, Ivo and Engelhardt, Sandy and Maier-Hein, Klaus H},
  booktitle={International workshop on statistical atlases and computational models of the heart},
  pages={120--129},
  year={2017},
  organization={Springer}
}

@inproceedings{wang2020ica,
  title={Ica-unet: Ica inspired statistical unet for real-time 3d cardiac cine mri segmentation},
  author={Wang, Tianchen and Xu, Xiaowei and Xiong, Jinjun and Jia, Qianjun and Yuan, Haiyun and Huang, Meiping and Zhuang, Jian and Shi, Yiyu},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={447--457},
  year={2020},
  organization={Springer}
}

%%%%%%%%%%%%%%%%%% Datasets and U-Net %%%%%%%%%%%%%%%%%%

@article{radau2009evaluation,
  title={Evaluation framework for algorithms segmenting short axis cardiac MRI},
  author={Perry Radau and Yingli Lu and Kim Connelly and Paul Graham and Alexander Dick and Graham Wright},
  journal={The MIDAS Journal-Cardiac MR Left Ventricle Segmentation Challenge},
  volume={49},
  year={2009}
}

@inproceedings{campello2020multi,
  title={Multi-centre multi-vendor \& multi-disease cardiac image segmentation challenge (m\&ms)},
  author={Campello, Victor M and Lekadir, K},
  booktitle={Medical Image Computing and Computer Assisted Intervention},
  year={2020}
}

@misc{perez_garcia_fernando_unet,
  author={P{\'e}rez-Garc{\'i}a, Fernando},
  title={{fepegar/unet: PyTorch implementation of 2D and 3D U-Net}},
  month={03},
  year=2020,
  publisher={Zenodo},
  version={v0.7.5},
  doi={10.5281/zenodo.3697931},
  url={https://doi.org/10.5281/zenodo.3697931}
}

@article{scikit_learn,
author = {Pedregosa, Fabian and Varoquaux, Gael and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, Edouard and Louppe, Gilles},
year = {2012},
month = {01},
pages = {},
title = {Scikit-learn: Machine Learning in Python},
volume = {12},
journal = {Journal of Machine Learning Research}
}



%%%%%%%%%%%%%%%%%% Uncertainty %%%%%%%%%%%%%%%%%%

@inproceedings{gal2016dropout,
  title={Dropout as a bayesian approximation: Representing model uncertainty in deep learning},
  author={Gal, Yarin and Ghahramani, Zoubin},
  booktitle={international conference on machine learning},
  pages={1050--1059},
  year={2016},
  organization={PMLR}
}

@inproceedings{guo2017calibration,
  title={On calibration of modern neural networks},
  author={Guo, Chuan and Pleiss, Geoff and Sun, Yu and Weinberger, Kilian Q},
  booktitle={International Conference on Machine Learning},
  pages={1321--1330},
  year={2017},
  organization={PMLR}
}

@inproceedings{blundell2015weight,
  title={Weight uncertainty in neural network},
  author={Blundell, Charles and Cornebise, Julien and Kavukcuoglu, Koray and Wierstra, Daan},
  booktitle={International Conference on Machine Learning},
  pages={1613--1622},
  year={2015},
  organization={PMLR}
}

@article{hoffman2013stochastic,
  title={Stochastic variational inference},
  author={Hoffman, Matthew D and Blei, David M and Wang, Chong and Paisley, John},
  journal={The Journal of Machine Learning Research},
  volume={14},
  number={1},
  pages={1303--1347},
  year={2013},
  publisher={JMLR. org}
}

@article{lakshminarayanan2017simple,
  title={Simple and scalable predictive uncertainty estimation using deep ensembles},
  author={Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
  journal={Advances in neural information processing systems},
  volume={30},
  pages={6402--6413},
  year={2017}
}

@inproceedings{NEURIPS2019_8558cb40,
 author = {Ovadia, Yaniv and Fertig, Emily and Ren, Jie and Nado, Zachary and Sculley, D. and Nowozin, Sebastian and Dillon, Joshua and Lakshminarayanan, Balaji and Snoek, Jasper},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
 pages = {13991--14002},
 publisher = {Curran Associates, Inc.},
 title = {Can you trust your model\textquotesingle s uncertainty?  Evaluating predictive uncertainty under dataset shift},
 volume = {32},
 year = {2019}
}

@inproceedings{jungo2019assessing,
  title={Assessing reliability and challenges of uncertainty estimations for medical image segmentation},
  author={Jungo, Alain and Reyes, Mauricio},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={48--56},
  year={2019},
  organization={Springer}
}

@article{jungo2020analyzing,
  title={Analyzing the quality and challenges of uncertainty estimations for brain tumor segmentation},
  author={Jungo, Alain and Balsiger, Fabian and Reyes, Mauricio},
  journal={Frontiers in neuroscience},
  volume={14},
  pages={282},
  year={2020},
  publisher={Frontiers}
}

@article{mehrtash2020confidence,
  title={Confidence calibration and predictive uncertainty estimation for deep medical image segmentation},
  author={Mehrtash, Alireza and Wells, William M and Tempany, Clare M and Abolmaesumi, Purang and Kapur, Tina},
  journal={IEEE transactions on medical imaging},
  volume={39},
  number={12},
  pages={3868--3878},
  year={2020},
  publisher={IEEE}
}

@inproceedings{kohl2018probabilistic,
  title={A probabilistic U-net for segmentation of ambiguous images},
  author={Kohl, Simon AA and Romera-Paredes, Bernardino and Meyer, Clemens and Fauw, Jeffrey De and Ledsam, Joseph R and Maier-Hein, Klaus H and Eslami, SM Ali and Rezende, Danilo Jimenez and Ronneberger, Olaf},
  booktitle={Proceedings of the 32nd International Conference on Neural Information Processing Systems},
  pages={6965--6975},
  year={2018}
}

@inproceedings{NEURIPS2020_95f8d990,
 author = {Monteiro, Miguel and Le Folgoc, Loic and Coelho de Castro, Daniel and Pawlowski, Nick and Marques, Bernardo and Kamnitsas, Konstantinos and van der Wilk, Mark and Glocker, Ben},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {12756--12767},
 publisher = {Curran Associates, Inc.},
 title = {Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty},
 volume = {33},
 year = {2020}
}



%%%%%%%%%%%%%%%%%% Self-supervision %%%%%%%%%%%%%%%%%%

@inproceedings{asano2019critical,
  title={A critical analysis of self-supervision, or what we can learn from a single image},
  author={Asano, YM and Rupprecht, C and Vedaldi, A},
  booktitle={International Conference on Learning Representations},
  year={2019}
}

@article{zhang2019survey,
  title={A survey of semi-and weakly supervised semantic segmentation of images},
  author={Zhang, Man and Zhou, Yong and Zhao, Jiaqi and Man, Yiyun and Liu, Bing and Yao, Rui},
  journal={Artificial Intelligence Review},
  pages={1--30},
  year={2019},
  publisher={Springer}
}

@inproceedings{noroozi2016unsupervised,
  title={Unsupervised learning of visual representations by solving jigsaw puzzles},
  author={Noroozi, Mehdi and Favaro, Paolo},
  booktitle={European Conference on Computer Vision},
  pages={69--84},
  year={2016},
  organization={Springer}
}

@inproceedings{vondrick2018tracking,
  title={Tracking emerges by colorizing videos},
  author={Vondrick, Carl and Shrivastava, Abhinav and Fathi, Alireza and Guadarrama, Sergio and Murphy, Kevin},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={391--408},
  year={2018}
}

@inproceedings{xie2020instance,
  title={Instance-Aware Self-supervised Learning for Nuclei Segmentation},
  author={Xie, Xinpeng and Chen, Jiawei and Li, Yuexiang and Shen, Linlin and Ma, Kai and Zheng, Yefeng},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={341--350},
  year={2020},
  organization={Springer}
}

@inproceedings{chu2020pay,
  title={Pay More Attention to Discontinuity for Medical Image Segmentation},
  author={Chu, Jiajia and Chen, Yajie and Zhou, Wei and Shi, Heshui and Cao, Yukun and Tu, Dandan and Jin, Richu and Xu, Yongchao},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={166--175},
  year={2020},
  organization={Springer}
}

@article{chen2018self,
  title={Self-supervised generative adversarial networks},
  author={Chen, Ting and Zhai, Xiaohua and Ritter, Marvin and Lucic, Mario and Houlsby, Neil},
  journal={arXiv preprint arXiv:1811.11212},
  volume={2},
  year={2018},
  publisher={Nov}
}

@inproceedings{su2020does,
  title={When does self-supervision improve few-shot learning?},
  author={Su, Jong-Chyi and Maji, Subhransu and Hariharan, Bharath},
  booktitle={European Conference on Computer Vision},
  pages={645--666},
  year={2020},
  organization={Springer}
}

%%%%%%%%%%%%%%%%%% O.o.d detection %%%%%%%%%%%%%%%%%%

@article{liu2020energy,
  title={Energy-based Out-of-distribution Detection},
  author={Liu, Weitang and Wang, Xiaoyun and Owens, John D and Li, Yixuan},
  journal={arXiv preprint arXiv:2010.03759},
  year={2020}
}

@article{hendrycks2016baseline,
  title={A baseline for detecting misclassified and out-of-distribution examples in neural networks},
  author={Hendrycks, Dan and Gimpel, Kevin},
  journal={arXiv preprint arXiv:1610.02136},
  year={2016}
}

@inproceedings{liang2018enhancing,
  title={Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks},
  author={Liang, Shiyu and Li, Yixuan and Srikant, R},
  booktitle={International Conference on Learning Representations},
  year={2018}
}

@inproceedings{hendrycks2018deep,
  title={Deep Anomaly Detection with Outlier Exposure},
  author={Hendrycks, Dan and Mazeika, Mantas and Dietterich, Thomas},
  booktitle={International Conference on Learning Representations},
  year={2018}
}

@inproceedings{lee2018simple,
  title={A simple unified framework for detecting out-of-distribution samples and adversarial attacks},
  author={Lee, Kimin and Lee, Kibok and Lee, Honglak and Shin, Jinwoo},
  booktitle={Advances in Neural Information Processing Systems},
  pages={7167--7177},
  year={2018}
}
@inproceedings{lee2018training,
  title={Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples},
  author={Lee, Kimin and Lee, Honglak and Lee, Kibok and Shin, Jinwoo},
  booktitle={International Conference on Learning Representations},
  year={2018}
}

@article{shafaei2018less,
  title={A less biased evaluation of out-of-distribution sample detectors},
  author={Shafaei, Alireza and Schmidt, Mark and Little, James J},
  journal={arXiv preprint arXiv:1809.04729},
  year={2018}
}

@inproceedings{oberdiek2020detection,
  title={Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation},
  author={Oberdiek, Philipp and Rottmann, Matthias and Fink, Gernot A},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages={328--329},
  year={2020}
}

@inproceedings{ahmed2020detecting,
  title={Detecting semantic anomalies},
  author={Ahmed, Faruk and Courville, Aaron},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={04},
  pages={3154--3162},
  year={2020}
}

@inproceedings{bevandic2019simultaneous,
  title={Simultaneous semantic segmentation and outlier detection in presence of domain shift},
  author={Bevandi{\'c}, Petra and Kre{\v{s}}o, Ivan and Or{\v{s}}i{\'c}, Marin and {\v{S}}egvi{\'c}, Sini{\v{s}}a},
  booktitle={German Conference on Pattern Recognition},
  pages={33--47},
  year={2019},
  organization={Springer}
}

@article{yan2020mri,
  title={Mri manufacturer shift and adaptation: Increasing the generalizability of deep learning segmentation for mr images acquired with different scanners},
  author={Yan, Wenjun and Huang, Lu and Xia, Liming and Gu, Shengjia and Yan, Fuhua and Wang, Yuanyuan and Tao, Qian},
  journal={Radiology: Artificial Intelligence},
  volume={2},
  number={4},
  pages={e190195},
  year={2020},
  publisher={Radiological Society of North America}
}

%%%%%%%%%%%%%%%%%% O.o.d with self-supervision %%%%%%%%%%%%%%%%%%

@inproceedings{xia2020synthesize,
  title={Synthesize then compare: Detecting failures and anomalies for semantic segmentation},
  author={Xia, Yingda and Zhang, Yi and Liu, Fengze and Shen, Wei and Yuille, Alan L},
  booktitle={European Conference on Computer Vision},
  pages={145--161},
  year={2020},
  organization={Springer}
}

@inproceedings{golan2018deep,
  title={Deep anomaly detection using geometric transformations},
  author={Golan, Izhak and El-Yaniv, Ran},
  booktitle={Advances in Neural Information Processing Systems},
  pages={9758--9769},
  year={2018}
}

@article{winkens2020contrastive,
  title={Contrastive training for improved out-of-distribution detection},
  author={Winkens, Jim and Bunel, Rudy and Roy, Abhijit Guha and Stanforth, Robert and Natarajan, Vivek and Ledsam, Joseph R and MacWilliams, Patricia and Kohli, Pushmeet and Karthikesalingam, Alan and Kohl, Simon and others},
  journal={arXiv preprint arXiv:2007.05566},
  year={2020}
}

@inproceedings{sahu2020endo,
  title={Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation},
  author={Sahu, Manish and Str{\"o}msd{\"o}rfer, Ronja and Mukhopadhyay, Anirban and Zachow, Stefan},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={784--794},
  year={2020},
  organization={Springer}
}

@article{wu2020simple,
  title={A Simple Framework for Uncertainty in Contrastive Learning},
  author={Wu, Mike and Goodman, Noah},
  journal={arXiv preprint arXiv:2010.02038},
  year={2020}
}

@inproceedings{vyas2018out,
  title={Out-of-distribution detection using an ensemble of self supervised leave-out classifiers},
  author={Vyas, Apoorv and Jammalamadaka, Nataraj and Zhu, Xia and Das, Dipankar and Kaul, Bharat and Willke, Theodore L},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={550--564},
  year={2018}
}

@inproceedings{yu2019unsupervised,
  title={Unsupervised out-of-distribution detection by maximum classifier discrepancy},
  author={Yu, Qing and Aizawa, Kiyoharu},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={9518--9526},
  year={2019}
}

@article{hendrycks2019using,
  title={Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty},
  author={Hendrycks, Dan and Mazeika, Mantas and Kadavath, Saurav and Song, Dawn},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  pages={15663--15674},
  year={2019}
}

@inproceedings{mohseni2020self,
  title={Self-Supervised Learning for Generalizable Out-of-Distribution Detection.},
  author={Mohseni, Sina and Pitale, Mandar and Yadawa, JBS and Wang, Zhangyang},
  booktitle={AAAI},
  pages={5216--5223},
  year={2020}
}

@inproceedings{pidhorskyi2018generative,
  title={Generative probabilistic novelty detection with adversarial autoencoders},
  author={Pidhorskyi, Stanislav and Almohsen, Ranya and Doretto, Gianfranco},
  booktitle={Advances in neural information processing systems},
  pages={6822--6833},
  year={2018}
}

@inproceedings{zhan2018mix,
  title={Mix-and-match tuning for self-supervised semantic segmentation},
  author={Zhan, Xiaohang and Liu, Ziwei and Luo, Ping and Tang, Xiaoou and Loy, Chen},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={32},
  number={1},
  year={2018}
}

@inproceedings{wang2020self,
  title={Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation},
  author={Wang, Yude and Zhang, Jie and Kan, Meina and Shan, Shiguang and Chen, Xilin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12275--12284},
  year={2020}
}

@inproceedings{pan2020unsupervised,
  title={Unsupervised intra-domain adaptation for semantic segmentation through self-supervision},
  author={Pan, Fei and Shin, Inkyu and Rameau, Francois and Lee, Seokju and Kweon, In So},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3764--3773},
  year={2020}
}

@article{canny1986computational,
  title={A computational approach to edge detection},
  author={Canny, John},
  journal={IEEE Transactions on pattern analysis and machine intelligence},
  volume={6},
  pages={679--698},
  year={1986},
  publisher={Ieee}
}

@article{perez_garcia_torchio_2020,
    title = {{TorchIO}: a {Python} library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning},
    shorttitle = {{TorchIO}},
    url = {http://arxiv.org/abs/2003.04696},
    urldate = {2020-03-11},
    journal = {arXiv:2003.04696 [cs, eess, stat]},
    author = {P{\'e}rez-Garc{\'i}a, Fernando and Sparks, Rachel and Ourselin, Sebastien},
    month = {03},
    year = {2020},
    note = {arXiv: 2003.04696},
    keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning},
}

@inproceedings{hein2019relu,
  title={Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem},
  author={Hein, Matthias and Andriushchenko, Maksym and Bitterwolf, Julian},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={41--50},
  year={2019}
}

@inproceedings{klingner2020improved,
  title={Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation},
  author={Klingner, Marvin and Bar, Andreas and Fingscheidt, Tim},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages={320--321},
  year={2020}
}

@article{glocker2019machine,
  title={Machine learning with multi-site imaging data: An empirical study on the impact of scanner effects},
  author={Glocker, Ben and Robinson, Robert and Castro, Daniel C and Dou, Qi and Konukoglu, Ender},
  journal={arXiv preprint arXiv:1910.04597},
  year={2019}
}

%%%%%%%%%%%%%%%%%% Template sample references %%%%%%%%%%%%%%%%%%


@book{Lamport:Book:1989,
 author = {Lamport, Leslie},
 title = {Latex: A Document Preparation System},
 year = {1986},
 isbn = {0-201-15790-X},
 publisher = {Addison-Wesley Longman Publishing Co., Inc.},
 address = {Boston, MA, USA},
}