@inproceedings{huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4700--4708},
  year={2017}
}
@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}
}
@inproceedings{lakshminarayanan2017simple,
  title={Simple and scalable predictive uncertainty estimation using deep ensembles},
  author={Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
  booktitle={Advances in Neural Information Processing Systems},
  pages={6402--6413},
  year={2017}
}
@article{ovadia2019can,
  title={Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift},
  author={Ovadia, Yaniv and Fertig, Emily and Ren, Jie and Nado, Zachary and Sculley, D and Nowozin, Sebastian and Dillon, Joshua V and Lakshminarayanan, Balaji and Snoek, Jasper},
  journal={Advances in Neural Information Processing Systems 32},
  pages={13991--14002},
  year={2019}
}

@inproceedings{guo2017calibration,
  title={On calibration of modern neural networks},
  author={Guo, Chuan and Pleiss, Geoff and Sun, Yu and Weinberger, Kilian Q},
  booktitle={Proceedings of the 34th International Conference on Machine Learning-Volume 70},
  pages={1321--1330},
  year={2017},
  organization={JMLR. org}
}
@inproceedings{rupprecht2017learning,
  title={Learning in an uncertain world: Representing ambiguity through multiple hypotheses},
  author={Rupprecht, Christian and Laina, Iro and DiPietro, Robert and Baust, Maximilian and Tombari, Federico and Navab, Nassir and Hager, Gregory D},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={3591--3600},
  year={2017}
}
@article{zhai2016deep,
  title={Deep structured energy based models for anomaly detection},
  author={Zhai, Shuangfei and Cheng, Yu and Lu, Weining and Zhang, Zhongfei},
  journal={arXiv preprint arXiv:1605.07717},
  year={2016}
}
@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{hendrycks2018deep,
  title={Deep anomaly detection with outlier exposure},
  author={Hendrycks, Dan and Mazeika, Mantas and Dietterich, Thomas G},
  journal={arXiv preprint arXiv:1812.04606},
  year={2018}
}
@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}
}
@article{bejnordi2017diagnostic,
  title={Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer},
  author={Bejnordi, Babak Ehteshami and Veta, Mitko and Van Diest, Paul Johannes and Van Ginneken, Bram and Karssemeijer, Nico and Litjens, Geert and Van Der Laak, Jeroen AWM and Hermsen, Meyke and Manson, Quirine F and Balkenhol, Maschenka and others},
  journal={Jama},
  volume={318},
  number={22},
  pages={2199--2210},
  year={2017},
  publisher={American Medical Association}
}
@article{liu2017detecting,
  title={Detecting cancer metastases on gigapixel pathology images},
  author={Liu, Yun and Gadepalli, Krishna and Norouzi, Mohammad and Dahl, George E and Kohlberger, Timo and Boyko, Aleksey and Venugopalan, Subhashini and Timofeev, Aleksei and Nelson, Philip Q and Corrado, Greg S and others},
  journal={arXiv preprint arXiv:1703.02442},
  year={2017}
}
@article{shafaei2018does,
  title={Does Your Model Know the Digit 6 Is Not a Cat? A Less Biased Evaluation of" Outlier" Detectors},
  author={Shafaei, Alireza and Schmidt, Mark and Little, James J},
  journal={arXiv preprint arXiv:1809.04729},
  year={2018}
}
@article{liang2017enhancing,
  title={Enhancing the reliability of out-of-distribution image detection in neural networks},
  author={Liang, Shiyu and Li, Yixuan and Srikant, Rayadurgam},
  journal={arXiv preprint arXiv:1706.02690},
  year={2017}
}
@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}
}
@article{fox2010incidental,
  title={Incidental lymphoma during sentinel lymph node biopsy for breast cancer.},
  author={Fox, Justin P and Grignol, Valerie P and Gustafson, J and Cheng, P and Weighall, R and Ouellette, J and Hellan, M and Dowdy, Y and Termuhlen, P},
  journal={Journal of Clinical Oncology},
  volume={28},
  number={15\_suppl},
  pages={e11083--e11083},
  year={2010},
  publisher={American Society of Clinical Oncology}
}
@article{karimi2019accurate,
  title={Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images},
  author={Karimi, Davood and Zeng, Qi and Mathur, Prateek and Avinash, Apeksha and Mahdavi, Sara and Spadinger, Ingrid and Abolmaesumi, Purang and Salcudean, Septimiu E},
  journal={Medical image analysis},
  volume={57},
  pages={186--196},
  year={2019},
  publisher={Elsevier}
}
@article{seebock2019exploiting,
  title={Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT},
  author={Seebock, Philipp and Orlando, Jos{\'e} Ignacio and Schlegl, Thomas and Waldstein, Sebastian M and Bogunovic, Hrvoje and Klimscha, Sophie and Langs, Georg and Schmidt-Erfurth, Ursula},
  journal={IEEE transactions on medical imaging},
  year={2019},
  publisher={IEEE}
}
@article{leibig2017leveraging,
  title={Leveraging uncertainty information from deep neural networks for disease detection},
  author={Leibig, Christian and Allken, Vaneeda and Ayhan, Murat Se{\c{c}}kin and Berens, Philipp and Wahl, Siegfried},
  journal={Scientific reports},
  volume={7},
  number={1},
  pages={17816},
  year={2017},
  publisher={Nature Publishing Group}
}
@article{kwon2020uncertainty,
  title={Uncertainty quantification using bayesian neural networks in classification: Application to biomedical image segmentation},
  author={Kwon, Yongchan and Won, Joong-Ho and Kim, Beom Joon and Paik, Myunghee Cho},
  journal={Computational Statistics \& Data Analysis},
  volume={142},
  pages={106816},
  year={2020},
  publisher={Elsevier}
}
@article{nair2020exploring,
  title={Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation},
  author={Nair, Tanya and Precup, Doina and Arnold, Douglas L and Arbel, Tal},
  journal={Medical image analysis},
  volume={59},
  pages={101557},
  year={2020},
  publisher={Elsevier}
}
@inproceedings{ghesu2019quantifying,
  title={Quantifying and leveraging classification uncertainty for chest radiograph assessment},
  author={Ghesu, Florin C and Georgescu, Bogdan and Gibson, Eli and Guendel, Sebastian and Kalra, Mannudeep K and Singh, Ramandeep and Digumarthy, Subba R and Grbic, Sasa and Comaniciu, Dorin},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={676--684},
  year={2019},
  organization={Springer}
}
@article{wang2016deep,
  title={Deep learning for identifying metastatic breast cancer},
  author={Wang, Dayong and Khosla, Aditya and Gargeya, Rishab and Irshad, Humayun and Beck, Andrew H},
  journal={arXiv preprint arXiv:1606.05718},
  year={2016}
}
@inproceedings{kingma:adam,
author = {Kingma, Diederick P and Ba, Jimmy},
booktitle = {International Conference on Learning Representations (ICLR)},
title = {{Adam: A method for stochastic optimization}},
year = {2015}
}
@inproceedings{tompson2015efficient,
  title={Efficient object localization using convolutional networks},
  author={Tompson, Jonathan and Goroshin, Ross and Jain, Arjun and LeCun, Yann and Bregler, Christoph},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={648--656},
  year={2015}
}
@article{bandi2019resolution,
  title={Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks},
  author={B{\'a}ndi, P{\'e}ter and Balkenhol, Maschenka and van Ginneken, Bram and van der Laak, Jeroen and Litjens, Geert},
  journal={PeerJ},
  volume={7},
  pages={e8242},
  year={2019},
  publisher={PeerJ Inc.}
}
@inproceedings{kendall2017uncertainties,
  title={What uncertainties do we need in bayesian deep learning for computer vision?},
  author={Kendall, Alex and Gal, Yarin},
  booktitle={Advances in neural information processing systems},
  pages={5574--5584},
  year={2017}
}
@article{der2009aleatory,
  title={Aleatory or epistemic? Does it matter?},
  author={Der Kiureghian, Armen and Ditlevsen, Ove},
  journal={Structural safety},
  volume={31},
  number={2},
  pages={105--112},
  year={2009},
  publisher={Elsevier}
}
@inproceedings{long2015fully,
  title={Fully convolutional networks for semantic segmentation},
  author={Long, Jonathan and Shelhamer, Evan and Darrell, Trevor},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={3431--3440},
  year={2015}
}
@article{lee2015m,
  title={Why M heads are better than one: Training a diverse ensemble of deep networks},
  author={Lee, Stefan and Purushwalkam, Senthil and Cogswell, Michael and Crandall, David and Batra, Dhruv},
  journal={arXiv preprint arXiv:1511.06314},
  year={2015}
}
@inproceedings{osband2016deep,
  title={Deep exploration via bootstrapped DQN},
  author={Osband, Ian and Blundell, Charles and Pritzel, Alexander and Van Roy, Benjamin},
  booktitle={Advances in neural information processing systems},
  pages={4026--4034},
  year={2016}
}