% Encoding: x-MacRoman


@inproceedings{ganin2015unsupervised,
  title={Unsupervised Domain Adaptation by Backpropagation},
  author={Ganin, Yaroslav and Lempitsky, Victor},
  booktitle={International Conference on Machine Learning},
  pages={1180--1189},
  year={2015}
}



@inproceedings{sun2016deep,
  title={Deep {CORAL}: Correlation alignment for deep domain adaptation},
  author={Sun, Baochen and Saenko, Kate},
  booktitle={European Conference on Computer Vision},
  pages={443--450},
  year={2016},
  organization={Springer}
}

@article{dolz20183d,
  title={3{D} fully convolutional networks for subcortical segmentation in {MRI}: {A} large-scale study},
  author={Dolz, Jose and Desrosiers, Christian and Ben Ayed, Ismail},
  journal={NeuroImage},
  volume={170},
  pages={456--470},
  year={2018},
  publisher={Elsevier}
}

@article{dolz2018hyperdense,
  title={HyperDense-Net: A hyper-densely connected {CNN} for multi-modal image segmentation},
  author={Dolz, Jose and Gopinath, Karthik and Yuan, Jing and Lombaert, Herve and Desrosiers, Christian and Ayed, Ismail Ben},
  journal={IEEE Transactions on Medical Imaging},
  year={2018},
  publisher={IEEE}
}

@inproceedings{javanmardi2018domain,
  title={Domain adaptation for biomedical image segmentation using adversarial training},
  author={Javanmardi, Mehran and Tasdizen, Tolga},
  booktitle={2018 IEEE 15th International Symposium on Biomedical Imaging},
  pages={554--558},
  year={2018},
  organization={IEEE}
}

@inproceedings{gholami2018novel,
  title={A Novel Domain Adaptation Framework for Medical Image Segmentation},
  author={Gholami, Amir and Subramanian, Shashank and Shenoy, Varun and Himthani, Naveen and Yue, Xiangyu and Zhao, Sicheng and Jin, Peter and Biros, George and Keutzer, Kurt},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={289--298},
  year={2018},
  organization={Springer}
}

@article{xu2018webly,
  title={Webly-supervised fine-grained visual categorization via deep domain adaptation},
  author={Xu, Zhe and Huang, Shaoli and Zhang, Ya and Tao, Dacheng},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={40},
  number={5},
  pages={1100--1113},
  year={2018},
  publisher={IEEE}
}

@article{Jebara2004,
  title={Probability Product Kernels}, 
  author={Jebara, Tony and Kondor, Risi and Howard, Andrew},
  journal={Journal of Machine Learning Research},
  volume={5},
  pages={819--844},
  year={2004}
}

@article{Oliveira-Tavares-14,
  title={Medical image registration: a review}, 
  author={Oliveira, Francisco P.M. and Tavares, Joao Manuel R.S.},
  journal={Computer Methods in Biomechanics and Biomedical Engineering},
  volume={17},
  number={2},
  pages={73--93},
  year={2014}
}




@inproceedings{shu2018dirt,
  title={A DirtT-T approach to unsupervised domain adaptation},
  author={Shu, Rui and Bui, Hung H and Narui, Hirokazu and Ermon, Stefano},
  booktitle={Proc. International Conference on Learning Representations},
  year={2018}
}



@inproceedings{long2015fully,
  title={Fully convolutional networks for semantic segmentation},
  author={Long, Jonathan and Shelhamer, Evan and Darrell, Trevor},
  booktitle={Proc. IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3431--3440},
  year={2015}
}

@article{litjens2017survey,
  title={A survey on deep learning in medical image analysis},
  author={Litjens, Geert and Kooi, Thijs and Bejnordi, Babak Ehteshami and Setio, Arnaud Arindra Adiyoso and Ciompi, Francesco and Ghafoorian, Mohsen and Van Der Laak, Jeroen Awm and Van Ginneken, Bram and S{\'a}nchez, Clara I},
  journal={Medical Image Analysis},
  volume={42},
  pages={60--88},
  year={2017},
  publisher={Elsevier}
}

@inproceedings{bousmalis2017unsupervised,
  title={Unsupervised pixel-level domain adaptation with generative adversarial networks},
  author={Bousmalis, Konstantinos and Silberman, Nathan and Dohan, David and Erhan, Dumitru and Krishnan, Dilip},
  booktitle={Proc. IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3722--3731},
  year={2017}
}

@inproceedings{chen2018semantic,
  title={Semantic-aware generative adversarial nets for unsupervised domain adaptation in chest x-ray segmentation},
  author={Chen, Cheng and Dou, Qi and Chen, Hao and Heng, Pheng-Ann},
  booktitle={Proc. International Workshop on Machine Learning in Medical Imaging},
  pages={143--151},
  year={2018},
  organization={Springer}
}

@INPROCEEDINGS{Mescheder2018ICML,
  author = {Lars Mescheder and Sebastian Nowozin and Andreas Geiger},
  title = {Which Training Methods for GANs do actually Converge?},
  booktitle = {Proc. International Conference on Machine Learning},
  year = {2018}
}

@inproceedings{zhang2018task,
  title={Task driven generative modeling for unsupervised domain adaptation: Application to x-ray image segmentation},
  author={Zhang, Yue and Miao, Shun and Mansi, Tommaso and Liao, Rui},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={599--607},
  year={2018},
  organization={Springer}
}

@article{mahmood2018unsupervised,
  title={Unsupervised reverse domain adaptation for synthetic medical images via adversarial training},
  author={Mahmood, Faisal and Chen, Richard and Durr, Nicholas J},
  journal={IEEE Transactions on Medical Imaging},
  volume={37},
  number={12},
  pages={2572--2581},
  year={2018},
  publisher={IEEE}
}

@inproceedings{murez2018image,
  title={Image to image translation for domain adaptation},
  author={Murez, Zak and Kolouri, Soheil and Kriegman, David and Ramamoorthi, Ravi and Kim, Kyungnam},
  booktitle={Proc. IEEE Conference on Computer Vision and Pattern Recognition},
  pages={4500--4509},
  year={2018}
}

@inproceedings{saito2018maximum,
  title={Maximum classifier discrepancy for unsupervised domain adaptation},
  author={Saito, Kuniaki and Watanabe, Kohei and Ushiku, Yoshitaka and Harada, Tatsuya},
  booktitle={Proc. IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3723--3732},
  year={2018}
}

@article{klein2010elastix,
  title={Elastix: a toolbox for intensity-based medical image registration},
  author={Klein, Stefan and Staring, Marius and Murphy, Keelin and Viergever, Max A and Pluim, Josien PW},
  journal={IEEE Transactions on Medical Imaging},
  volume={29},
  number={1},
  pages={196--205},
  year={2010},
  publisher={IEEE}
}

@article{chitta2018adaptive,
  title={Adaptive Semantic Segmentation with a Strategic Curriculum of Proxy Labels},
  author={Chitta, Kashyap and Feng, Jianwei and Hebert, Martial},
  journal={arXiv preprint arXiv:1811.03542},
  year={2018}
}

@inproceedings{russo2018source,
  title={From source to target and back: symmetric bi-directional adaptive {GAN}},
  author={Russo, Paolo and Carlucci, Fabio M and Tommasi, Tatiana and Caputo, Barbara},
  booktitle={Proc. IEEE Conference on Computer Vision and Pattern Recognition},
  pages={8099--8108},
  year={2018}
}

@inproceedings{shrivastava2017learning,
  title={Learning from simulated and unsupervised images through adversarial training},
  author={Shrivastava, Ashish and Pfister, Tomas and Tuzel, Oncel and Susskind, Joshua and Wang, Wenda and Webb, Russell},
  booktitle={Proc. IEEE Conference on Computer Vision and Pattern Recognition},
  pages={2107--2116},
  year={2017}
}

@inproceedings{goodfellow2014generative,
  title={Generative adversarial nets},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  booktitle={Advances in neural information processing systems},
  pages={2672--2680},
  year={2014}
}

@inproceedings{yan2017mind,
  title={Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation},
  author={Yan, Hongliang and Ding, Yukang and Li, Peihua and Wang, Qilong and Xu, Yong and Zuo, Wangmeng},
  booktitle={Proc. IEEE Conference on Computer Vision and Pattern Recognition},
  pages={2272--2281},
  year={2017}
}

@inproceedings{zou2018unsupervised,
  title={Unsupervised domain adaptation for semantic segmentation via class-balanced self-training},
  author={Zou, Yang and Yu, Zhiding and Vijaya Kumar, BVK and Wang, Jinsong},
  booktitle={Proc. European Conference on Computer Vision},
  pages={289--305},
  year={2018}
}

@article{pichler2019DDM,
  title={On Direct Distribution Matching for Adapting Segmentation Networks},
  author={Pichler, Georg and Dolz, Jose and Ben Ayed, Ismail and Piantinada, Pablo},
  journal={arXiv preprint arXiv:1904.02657},
  year={2019}
}

@inproceedings{long2015learning,
  title={Learning transferable features with deep adaptation networks},
  author={Long, Mingsheng and Cao, Yue and Wang, Jianmin and Jordan, Michael I},
  booktitle={International Conference on Machine Learning},
  year={2015}
}

@inproceedings{long2016unsupervised,
  title={Unsupervised domain adaptation with residual transfer networks},
  author={Long, Mingsheng and Zhu, Han and Wang, Jianmin and Jordan, Michael I},
  booktitle={Advances in Neural Information Processing Systems},
  pages={136--144},
  year={2016}
}

@inproceedings{tzeng2015simultaneous,
  title={Simultaneous deep transfer across domains and tasks},
  author={Tzeng, Eric and Hoffman, Judy and Darrell, Trevor and Saenko, Kate},
  booktitle={Proc. IEEE International Conference on Computer Vision},
  pages={4068--4076},
  year={2015}
}

@article{mendrik2015mrbrains,
  title={MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans},
  author={Mendrik, Adri{\"e}nne M and Vincken, Koen L and Kuijf, Hugo J and Breeuwer, Marcel and Bouvy, Willem H and De Bresser, Jeroen and Alansary, Amir and De Bruijne, Marleen and Carass, Aaron and El-Baz, Ayman and others},
  journal={Computational intelligence and neuroscience},
  volume={2015},
  pages={1},
  year={2015},
  publisher={Hindawi Publishing Corp.}
}

@article{iSEG, 
author={L. {Wang} and D. {Nie} and G. {Li} and \'{E}. {Puybareau} and J. {Dolz} and Q. {Zhang} and F. {Wang} and J. {Xia} and Z. {Wu} and J. {Chen} and K. {Thung} and T. D. {Bui} and J. {Shin} and G. {Zeng} and G. {Zheng} and V. S. {Fonov} and A. {Doyle} and Y. {Xu} and P. {Moeskops} and J. P. W. {Pluim} and C. {Desrosiers} and I. {Ben Ayed} and G. {Sanroma} and O. M. {Benkarim} and A. {Casamitjana} and V. {Vilaplana} and W. {Lin} and G. {Li} and D. {Shen}}, 
journal={IEEE Transactions on Medical Imaging}, 
title={Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge}, 
year={2019}, 
volume={}, 
number={}, 
pages={1-1}, 
keywords={Image segmentation;Magnetic resonance imaging;Manuals;Pediatrics;Biomedical imaging;Testing;White matter}, 
doi={10.1109/TMI.2019.2901712}, 
ISSN={0278-0062}, 
month={},}

@inproceedings{wu2018dcan,
  title={D{CAN}: Dual channel-wise alignment networks for unsupervised scene adaptation},
  author={Wu, Zuxuan and Han, Xintong and Lin, Yen-Liang and Gokhan Uzunbas, Mustafa and Goldstein, Tom and Nam Lim, Ser and Davis, Larry S},
  booktitle={Proc. European Conference on Computer Vision},
  pages={518--534},
  year={2018}
}

@article{ganin2016domain,
  title={Domain-adversarial training of neural networks},
  author={Ganin, Yaroslav and Ustinova, Evgeniya and Ajakan, Hana and Germain, Pascal and Larochelle, Hugo and Laviolette, Fran{\c{c}}ois and Marchand, Mario and Lempitsky, Victor},
  journal={The Journal of Machine Learning Research},
  volume={17},
  number={1},
  pages={2096--2030},
  year={2016},
  publisher={JMLR. org}
}

@inproceedings{tzeng2017adversarial,
  title={Adversarial discriminative domain adaptation},
  author={Tzeng, Eric and Hoffman, Judy and Saenko, Kate and Darrell, Trevor},
  booktitle={Proc. IEEE Conference on  Computer Vision and Pattern Recognition},
  pages={2962-2971},
  doi={10.1109/CVPR.2017.316},
  ISSN={1063-6919},
  month={July},
  year=2017
}

@inproceedings{hong2018conditional,
  title={Conditional generative adversarial network for structured domain adaptation},
  author={Hong, Weixiang and Wang, Zhenzhen and Yang, Ming and Yuan, Junsong},
  booktitle={Proc. IEEE Conference on Computer Vision and Pattern Recognition},
  pages={1335--1344},
  year={2018}
}

@inproceedings{liu2018detach,
  title={Detach and adapt: Learning cross-domain disentangled deep representation},
  author={Liu, Yen-Cheng and Yeh, Yu-Ying and Fu, Tzu-Chien and Wang, Sheng-De and Chiu, Wei-Chen and Frank Wang, Yu-Chiang},
  booktitle={Proc. IEEE Conference on Computer Vision and Pattern Recognition},
  pages={8867--8876},
  year={2018}
}

@inproceedings{chen2018road,
  title={Road: Reality oriented adaptation for semantic segmentation of urban scenes},
  author={Chen, Yuhua and Li, Wen and Van Gool, Luc},
  booktitle={Proc. IEEE Conference on Computer Vision and Pattern Recognition},
  pages={7892--7901},
  year={2018}
}

@inproceedings{chen2017no,
  title={No more discrimination: Cross city adaptation of road scene segmenters},
  author={Chen, Yi-Hsin and Chen, Wei-Yu and Chen, Yu-Ting and Tsai, Bo-Cheng and Frank Wang, Yu-Chiang and Sun, Min},
  booktitle={Proc. IEEE International Conference on Computer Vision},
  pages={1992--2001},
  year={2017}
}

@inproceedings{ghifary2016deep,
  title={Deep reconstruction-classification networks for unsupervised domain adaptation},
  author={Ghifary, Muhammad and Kleijn, W Bastiaan and Zhang, Mengjie and Balduzzi, David and Li, Wen},
  booktitle={European Conference on Computer Vision},
  pages={597--613},
  year={2016},
  organization={Springer}
}

@article{zhao2019supervised,
  title={Supervised Segmentation of Un-Annotated Retinal Fundus Images by Synthesis},
  author={Zhao, He and Li, Huiqi and Maurer-Stroh, Sebastian and Guo, Yuhong and Deng, Qiuju and Cheng, Li},
  journal={IEEE Transactions on Medical Imaging},
  volume={38},
  number={1},
  pages={46--56},
  year={2019},
  publisher={IEEE}
}

@article{Wachinger2016Domain,
    day = {01},
    journal = {NeuroImage},
    month = oct,
    pages = {470--479},
    title = {Domain adaptation for Alzheimer's disease diagnostics.},
    volume = {139},
    year = {2016}
}


@inproceedings{vanTulder2016Representation,
    author = {van Tulder, Gijs and de Bruijne, Marleen},
    booktitle = {MICCAI Medical Computer Vision},
    title = {Representation Learning for {Cross-Modality} Classification},
    year = {2016}
}

@inproceedings{vu2018advent,
  title={ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation},
  author={Vu, Tuan-Hung and Jain, Himalaya and Bucher, Maxime and Cord, Mathieu and P{\'e}rez, Patrick},
  booktitle={Proc. IEEE Conference on Computer Vision and Pattern Recognition},
  pages={2517--2526},
  year={2019}
}

@article{csurka2017domain,
  title={Domain adaptation for visual applications: A comprehensive survey},
  author={Csurka, Gabriela},
  journal={arXiv preprint arXiv:1702.05374},
  year={2017}
}

@inproceedings{sankaranarayanan2017generate,
  title={Generate to adapt: Aligning domains using generative adversarial networks},
  author={Sankaranarayanan, Swami and Balaji, Yogesh},
  booktitle={Computer Vision and Pattern Recognition},
  year={2018}
}

@article{hoffman2016fcns,
  title={{FCN}s in the wild: Pixel-level adversarial and constraint-based adaptation},
  author={Hoffman, Judy and Wang, Dequan and Yu, Fisher and Darrell, Trevor},
  journal={arXiv preprint arXiv:1612.02649},
  year={2016}
}

@inproceedings{kamnitsas2017unsupervised,
  title={Unsupervised domain adaptation in brain lesion segmentation with adversarial networks},
  author={Kamnitsas, Konstantinos and Baumgartner, Christian and Ledig, Christian and Newcombe, Virginia and Simpson, Joanna and Kane, Andrew and Menon, David and Nori, Aditya and Criminisi, Antonio and Rueckert, Daniel and others},
  booktitle={International Conference on Information Processing in Medical Imaging},
  pages={597--609},
  year={2017},
  organization={Springer}
}

@inproceedings{zhang2017curriculum,
  title={Curriculum domain adaptation for semantic segmentation of urban scenes},
  author={Zhang, Yang and David, Philip and Gong, Boqing},
  booktitle={Proc. IEEE International Conference on Computer Vision},
  pages={2020--2030},
  year={2017}
}

@article{degel2018domain,
  title={Domain and Geometry Agnostic {CNN}s for Left Atrium Segmentation in {3D} Ultrasound},
  author={Degel, Markus A and Navab, Nassir and Albarqouni, Shadi},
  journal={arXiv preprint arXiv:1805.00357},
  year={2018}
}

@inproceedings{hoffman2017cycada,
  title={Cycada: Cycle-consistent adversarial domain adaptation},
  author={Hoffman, Judy and Tzeng, Eric and Park, Taesung and Zhu, Jun-Yan and Isola, Phillip and Saenko, Kate and Efros, Alexei A and Darrell, Trevor},
  booktitle={International Conference on Machine Learning},
  pages={1989--1998},
  year={2018}
}

@article{huo2018synseg,
  title={SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth},
  author={Huo, Yuankai and Xu, Zhoubing and Moon, Hyeonsoo and Bao, Shunxing and Assad, Albert and Moyo, Tamara K and Savona, Michael R and Abramson, Richard G and Landman, Bennett A},
  journal={IEEE Transactions on Medical Imaging},
  year={2018},
  publisher={IEEE}
}

@article{kouw2018learning,
  title={Learning an MR acquisition-invariant representation using Siamese neural networks},
  author={Kouw, Wouter M and Loog, Marco and Bartels, Wilbert and Mendrik, Adri{\"e}nne M},
  journal={arXiv preprint arXiv:1810.07430},
  year={2018}
}

@inproceedings{dou2018unsupervised,
  title     = {Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss},
  author    = {Qi Dou and Cheng Ouyang and Cheng Chen and Hao Chen and Pheng-Ann Heng},
  booktitle = {Proc. International Joint Conference on Intelligence},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},             
  pages     = {691--697},
  year      = {2018},
  month     = {7},
  doi       = {10.24963/ijcai.2018/96},
  url       = {https://doi.org/10.24963/ijcai.2018/96},
}

@article{dou2018pnp,
  title={Pn{P}-{A}da{N}et: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation},
  author={Dou, Qi and Ouyang, Cheng and Chen, Cheng and Chen, Hao and Glocker, Ben and Zhuang, Xiahai and Heng, Pheng-Ann},
  journal={arXiv preprint arXiv:1812.07907},
  year={2018}
}

@article{gretton2012kernel,
  title={A kernel two-sample test},
  author={Gretton, Arthur and Borgwardt, Karsten M and Rasch, Malte J and Sch{\"o}lkopf, Bernhard and Smola, Alexander},
  journal={Journal of Machine Learning Research},
  volume={13},
  number={Mar},
  pages={723--773},
  year={2012}
}

@article{van2019learning,
  title={Learning Cross-Modality Representations from Multi-Modal Images},
  author={van Tulder, Gijs and de Bruijne, Marleen},
  journal={IEEE Transactions on Medical Imaging},
  volume={38},
  number={2},
  pages={638--648},
  year={2019},
  publisher={IEEE}
}

@article{zhu2019boundary,
  title={Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation},
  author={Zhu, Qikui and Du, Bo and Yan, Pingkun},
  journal={arXiv preprint arXiv:1902.08128},
  year={2019}
}

@article{tzeng2014deep,
  title={Deep domain confusion: Maximizing for domain invariance},
  author={Tzeng, Eric and Hoffman, Judy and Zhang, Ning and Saenko, Kate and Darrell, Trevor},
  journal={arXiv preprint arXiv:1412.3474},
  year={2014}
}

@article{van2015transfer,
  title={Transfer learning improves supervised image segmentation across imaging protocols},
  author={Van Opbroek, Annegreet and Ikram, M Arfan and Vernooij, Meike W and De Bruijne, Marleen},
  journal={IEEE Transactions on Medical Imaging},
  volume={34},
  number={5},
  pages={1018--1030},
  year={2015},
  publisher={IEEE}
}

@inproceedings{zhu2017unpaired,
  title={Unpaired image-to-image translation using cycle-consistent adversarial networks},
  author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
  booktitle={Proc. IEEE International Conference on Computer Vision},
  pages={2223--2232},
  year={2017}
}

@article{cai2019towards,
  title={Towards cross-modal organ translation and segmentation: A cycle-and shape-consistent generative adversarial network},
  author={Cai, Jinzheng and Zhang, Zizhao and Cui, Lei and Zheng, Yefeng and Yang, Lin},
  journal={Medical Image Analysis},
  volume={52},
  pages={174--184},
  year={2019},
  publisher={Elsevier}
}

@inproceedings{ghafoorian2017transfer,
  title={Transfer learning for domain adaptation in mri: Application in brain lesion segmentation},
  author={Ghafoorian, Mohsen and Mehrtash, Alireza and Kapur, Tina and Karssemeijer, Nico and Marchiori, Elena and Pesteie, Mehran and Guttmann, Charles RG and de Leeuw, Frank-Erik and Tempany, Clare M and van Ginneken, Bram and others},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={516--524},
  year={2017},
  organization={Springer}
}

@inproceedings{zhang2018translating,
  title={Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network},
  author={Zhang, Zizhao and Yang, Lin and Zheng, Yefeng},
  booktitle={Proc. IEEE Conference on Computer Vision and Pattern Recognition},
  pages={9242--9251},
  year={2018}
}

@inproceedings{chen2019synergistic,
  title={Synergistic image and feature adaptation: Towards cross-modality domain adaptation for medical image segmentation},
  author={Chen, Cheng and Dou, Qi and Chen, Hao and Qin, Jing and Heng, Pheng-Ann},
  booktitle={Proc. AAAI Conference on Artificial Intelligence},
  volume=33,
  pages={865--872},
  year=2019
}

@inproceedings{huo2018adversarial,
  title={Adversarial synthesis learning enables segmentation without target modality ground truth},
  author={Huo, Yuankai and Xu, Zhoubing and Bao, Shunxing and Assad, Albert and Abramson, Richard G and Landman, Bennett A},
  booktitle={Proc. IEEE International Symposium on Biomedical Imaging},
  pages={1217--1220},
  year={2018},
  organization={IEEE}
}


@inproceedings{tsai2018learning,
  title={Learning to adapt structured output space for semantic segmentation},
  author={Tsai, Yi-Hsuan and Hung, Wei-Chih and Schulter, Samuel and Sohn, Kihyuk and Yang, Ming-Hsuan and Chandraker, Manmohan},
  booktitle={Proc. Computer Vision and Pattern Recognition},
  year={2018}
}
@InProceedings{Ronneberger2015U,
  author        = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  title         = {U-{N}et: Convolutional networks for biomedical image segmentation},
  booktitle     = {International Conference on Medical image computing and computer-assisted intervention},
  year          = {2015},
  pages         = {234--241},
  organization  = {Springer},
  __markedentry = {[georg:]},
  owner         = {georg},
  timestamp     = {2019-03-06},
}

@Article{Lowekamp2013design,
  author        = {Lowekamp, Bradley Christopher and Chen, David T and Ib{\'a}{\~n}ez, Luis and Blezek, Daniel},
  title         = {The design of SimpleITK},
  journal       = {Frontiers in neuroinformatics},
  year          = {2013},
  volume        = {7},
  pages         = {45},
  __markedentry = {[georg:6]},
  owner         = {georg},
  publisher     = {Frontiers},
  timestamp     = {2019-03-06},
}

@Article{Kingma2014Adam,
  author      = {Diederik P. Kingma and Jimmy Ba},
  title       = {Adam: A Method for Stochastic Optimization},
  journal     = {Proc. International Conference on Learning Representations},
  abstract    = {We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.},
  year        = {2015},
  date        = {2014-12-22},
  eprint      = {http://arxiv.org/abs/1412.6980v9},
  eprintclass = {cs.LG},
  eprinttype  = {arXiv},
  file        = {:Kingma2014Adam - Adam_ a Method for Stochastic Optimization.pdf:PDF},
  keywords    = {cs.LG},
  owner       = {georg},
  timestamp   = {2019-02-15},
}

@article{mattes2003pet,
  title={{PET}-{CT} image registration in the chest using free-form deformations},
  author={Mattes, David and Haynor, David R. and Vesselle, Hubert and Lewellen, Thomas K. and Eubank, William},
  journal={IEEE Transactions on Medical Imaging},
  volume={22},
  number={1},
  pages={120--128},
  year={2003},
  publisher={IEEE}
}

@Comment{jabref-meta: databaseType:bibtex;}
