@article{lungtracking,
  title={{LungRegNet}: an unsupervised deformable image registration method for 4D-CT lung},
  author={Fu, Yabo and Lei, Yang and Wang, Tonghe and Higgins, Kristin and Bradley, Jeffrey D and Curran, Walter J and Liu, Tian and Yang, Xiaofeng},
  journal={Medical physics},
  volume={47},
  number={4},
  pages={1763--1774},
  year={2020},
  publisher={Wiley Online Library}
}

@article{cardiac-diagnosis,
  title={An overview on image registration techniques for cardiac diagnosis and treatment},
  author={Khalil, Azira and Ng, Siew-Cheok and Liew, Yih Miin and Lai, Khin Wee},
  journal={Cardiology research and practice},
  volume={2018},
  year={2018},
  publisher={Hindawi}
}

@article{progression-tracking,
author = {Viergever, Max and Maintz, J.B. and Klein, Stefan and Murphy, Keelin and Staring, Marius and Pluim, Josien},
year = {2016},
month = {06},
pages = {},
title = {A survey of medical image registration – under review},
volume = {33},
journal = {Medical Image Analysis},
doi = {10.1016/j.media.2016.06.030}
}

@article{voxelmorph,
  title={{VoxelMorph}: a learning framework for deformable medical image registration},
  author={Balakrishnan, Guha and Zhao, Amy and Sabuncu, Mert R and Guttag, John and Dalca, Adrian V},
  journal={IEEE transactions on medical imaging},
  volume={38},
  number={8},
  pages={1788--1800},
  year={2019},
  publisher={IEEE}
}

@article{hcp,
title = {The Human Connectome Project: A data acquisition perspective},
journal = {NeuroImage},
volume = {62},
number = {4},
pages = {2222-2231},
year = {2012},
note = {Connectivity},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2012.02.018},
url = {https://www.sciencedirect.com/science/article/pii/S1053811912001954},
author = {D.C. {Van Essen} and K. Ugurbil and E. Auerbach and D. Barch and T.E.J. Behrens and R. Bucholz and A. Chang and L. Chen and M. Corbetta and S.W. Curtiss and S. {Della Penna} and D. Feinberg and M.F. Glasser and N. Harel and A.C. Heath and L. Larson-Prior and D. Marcus and G. Michalareas and S. Moeller and R. Oostenveld and S.E. Petersen and F. Prior and B.L. Schlaggar and S.M. Smith and A.Z. Snyder and J. Xu and E. Yacoub},
keywords = {Connectivity, fMRI, Diffusion imaging, MEG/EEG, Twins, Behavior},
abstract = {The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.}
}

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   doi = {https://doi.org/10.1016/j.cmpb.2021.106236},
   url = {https://www.sciencedirect.com/science/article/pii/S0169260721003102},
   author = {P{\'e}rez-Garc{\'i}a, Fernando and Sparks, Rachel and Ourselin, S{\'e}bastien},
}

@article{ants,
  title={Advanced normalization tools (ANTS)},
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}

@inproceedings{convex-opt,
  title={Non-parametric discrete registration with convex optimisation},
  author={Heinrich, Mattias P and Papie{\.z}, Bartlomiej W and Schnabel, Julia A and Handels, Heinz},
  booktitle={Biomedical Image Registration: 6th International Workshop, WBIR 2014, London, UK, July 7-8, 2014. Proceedings 6},
  pages={51--61},
  year={2014},
  organization={Springer}
}

@article{intensity,
title = {Accurate and precise {2D–3D} registration based on {X-ray} intensity},
journal = {Computer Vision and Image Understanding},
volume = {110},
number = {1},
pages = {134-151},
year = {2008},
issn = {1077-3142},
doi = {https://doi.org/10.1016/j.cviu.2007.05.006},
url = {https://www.sciencedirect.com/science/article/pii/S1077314207000859},
author = {Souha Aouadi and Laurent Sarry},
keywords = {2D–3D registration, Object pose, Mutual information, Digital radiography, Computed tomography, Stochastic clustering},
}

@inproceedings{spline,
  title={Point-based elastic registration of medical image data using approximating thin-plate splines},
  author={Rohr, Karl and Stiehl, H Siegfried and Sprengel, Rainer and Beil, Wolfgang and Buzug, Thorsten M and Weese, J{\"u}rgen and Kuhn, MH},
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  year={1996},
  organization={Springer}
}

@ARTICLE{cnnregression,
  author={Miao, Shun and Wang, Z. Jane and Liao, Rui},
  journal={IEEE Transactions on Medical Imaging}, 
  title={A {CNN} Regression Approach for Real-Time {2D/3D} Registration}, 
  year={2016},
  volume={35},
  number={5},
  pages={1352-1363},
  doi={10.1109/TMI.2016.2521800}
}

@inproceedings{gradicon,
  title={{GradICON}: Approximate diffeomorphisms via gradient inverse consistency},
  author={Tian, Lin and Greer, Hastings and Vialard, Fran{\c{c}}ois-Xavier and Kwitt, Roland and Est{\'e}par, Ra{\'u}l San Jos{\'e} and Rushmore, Richard Jarrett and Makris, Nikolaos and Bouix, Sylvain and Niethammer, Marc},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={18084--18094},
  year={2023}
}

@inproceedings{icon,
  title={{ICON}: Learning regular maps through inverse consistency},
  author={Greer, Hastings and Kwitt, Roland and Vialard, Fran{\c{c}}ois-Xavier and Niethammer, Marc},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={3396--3405},
  year={2021}
}

@InProceedings{iconbyconstr,
author="Greer, Hastings
and Tian, Lin
and Vialard, Francois-Xavier
and Kwitt, Roland
and Bouix, Sylvain
and San Jose Estepar, Raul
and Rushmore, Richard
and Niethammer, Marc",
editor="Greenspan, Hayit
and Madabhushi, Anant
and Mousavi, Parvin
and Salcudean, Septimiu
and Duncan, James
and Syeda-Mahmood, Tanveer
and Taylor, Russell",
title="Inverse Consistency by Construction for Multistep Deep Registration",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="688--698",
}

@article{birnet,
  title={{BIRNet}: Brain image registration using dual-supervised fully convolutional networks},
  author={Fan, Jingfan and Cao, Xiaohuan and Yap, Pew-Thian and Shen, Dinggang},
  journal={Medical image analysis},
  volume={54},
  pages={193--206},
  year={2019},
  publisher={Elsevier}
}

@inproceedings{hypermorph,
  title={Hypermorph: Amortized hyperparameter learning for image registration},
  author={Hoopes, Andrew and Hoffmann, Malte and Fischl, Bruce and Guttag, John and Dalca, Adrian V},
  booktitle={Information Processing in Medical Imaging: 27th International Conference, IPMI 2021, Virtual Event, June 28--June 30, 2021, Proceedings 27},
  pages={3--17},
  year={2021},
  organization={Springer}
}

@article{cyclemorph,
  title={{CycleMorph}: cycle consistent unsupervised deformable image registration},
  author={Kim, Boah and Kim, Dong Hwan and Park, Seong Ho and Kim, Jieun and Lee, June-Goo and Ye, Jong Chul},
  journal={Medical image analysis},
  volume={71},
  pages={102036},
  year={2021},
  publisher={Elsevier}
}

@inproceedings{diffusemorph,
  title={{DiffuseMorph}: unsupervised deformable image registration using diffusion model},
  author={Kim, Boah and Han, Inhwa and Ye, Jong Chul},
  booktitle={European Conference on Computer Vision},
  pages={347--364},
  year={2022},
  organization={Springer}
}

@article{admir,
  title={{ADMIR}--affine and deformable medical image registration for drug-addicted brain images},
  author={Tang, Kun and Li, Zhi and Tian, Lili and Wang, Lihui and Zhu, Yuemin},
  journal={IEEE Access},
  volume={8},
  pages={70960--70968},
  year={2020},
  publisher={IEEE}
}

@article{MI,
  title={Multi-modal volume registration by maximization of mutual information},
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}

@article{mind,
  title={{MIND}: Modality independent neighbourhood descriptor for multi-modal deformable registration},
  author={Heinrich, Mattias P and Jenkinson, Mark and Bhushan, Manav and Matin, Tahreema and Gleeson, Fergus V and Brady, Michael and Schnabel, Julia A},
  journal={Medical image analysis},
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  number={7},
  pages={1423--1435},
  year={2012},
  publisher={Elsevier}
}

@inproceedings{ssc,
  title={The correlation ratio as a new similarity measure for multimodal image registration},
  author={Roche, Alexis and Malandain, Gr{\'e}goire and Pennec, Xavier and Ayache, Nicholas},
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  pages={1115--1124},
  year={1998},
  organization={Springer}
}

@inproceedings{gradient_field,
  title={Intensity gradient based registration and fusion of multi-modal images},
  author={Haber, Eldad and Modersitzki, Jan},
  booktitle={Medical Image Computing and Computer-Assisted Intervention--MICCAI 2006: 9th International Conference, Copenhagen, Denmark, October 1-6, 2006. Proceedings, Part II 9},
  pages={726--733},
  year={2006},
  organization={Springer}
}

@inproceedings{deep_atlas,
  title={{DeepAtlas}: Joint semi-supervised learning of image registration and segmentation},
  author={Xu, Zhenlin and Niethammer, Marc},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13--17, 2019, Proceedings, Part II 22},
  pages={420--429},
  year={2019},
  organization={Springer}
}

@article{segmentation_registration,
  title={Segmentation-based registration of ultrasound volumes for glioma resection in image-guided neurosurgery},
  author={Canalini, Luca and Klein, Jan and Miller, Dorothea and Kikinis, Ron},
  journal={International journal of computer assisted radiology and surgery},
  volume={14},
  pages={1697--1713},
  year={2019},
  publisher={Springer}
}

@article{segmentation_registration2,
  title={Symmetric diffeomorphic image registration with multi-label segmentation masks},
  author={Cai, Chenwei and Wang, Lvda and Ying, Shihui},
  journal={Mathematics},
  volume={10},
  number={11},
  pages={1946},
  year={2022},
  publisher={MDPI}
}

@inproceedings{cyclegan,
  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={Proceedings of the IEEE international conference on computer vision},
  pages={2223--2232},
  year={2017}
}

@inproceedings{pix2pix,
  title={Image-to-image translation with conditional adversarial networks},
  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1125--1134},
  year={2017}
}

@inproceedings{cycada,
  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 and Darrell, Trevor},
  booktitle={International conference on machine learning},
  pages={1989--1998},
  year={2018},
  organization={Pmlr}
}

@inproceedings{multitarget,
  title={Multi-target adversarial frameworks for domain adaptation in semantic segmentation},
  author={Saporta, Antoine and Vu, Tuan-Hung and Cord, Matthieu and P{\'e}rez, Patrick},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={9072--9081},
  year={2021}
}

@inproceedings{semi,
  title={Semi-supervised domain adaptation based on dual-level domain mixing for semantic segmentation},
  author={Chen, Shuaijun and Jia, Xu and He, Jianzhong and Shi, Yongjie and Liu, Jianzhuang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11018--11027},
  year={2021}
}

@inproceedings{semi-medical,
  title={Semi-supervised Domain Adaptive Medical Image Segmentation Through Consistency Regularized Disentangled Contrastive Learning},
  author={Basak, Hritam and Yin, Zhaozheng},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={260--270},
  year={2023},
  organization={Springer}
}

@inproceedings{resnet,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
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  pages={770--778},
  year={2016}
}

@inproceedings{unet,
  title={U-net: Convolutional networks for biomedical image segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle={Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18},
  pages={234--241},
  year={2015},
  organization={Springer}
}

@misc{monai,
title={{MONAI}: An open-source framework for deep learning in healthcare}, 
author={M. Jorge Cardoso and Wenqi Li and Richard Brown and Nic Ma and Eric Kerfoot and Yiheng Wang and Benjamin Murrey and Andriy Myronenko and Can Zhao and Dong Yang and Vishwesh Nath and Yufan He and Ziyue Xu and Ali Hatamizadeh and Andriy Myronenko and Wentao Zhu and Yun Liu and Mingxin Zheng and Yucheng Tang and Isaac Yang and Michael Zephyr and Behrooz Hashemian and Sachidanand Alle and Mohammad Zalbagi Darestani and Charlie Budd and Marc Modat and Tom Vercauteren and Guotai Wang and Yiwen Li and Yipeng Hu and Yunguan Fu and Benjamin Gorman and Hans Johnson and Brad Genereaux and Barbaros S. Erdal and Vikash Gupta and Andres Diaz-Pinto and Andre Dourson and Lena Maier-Hein and Paul F. Jaeger and Michael Baumgartner and Jayashree Kalpathy-Cramer and Mona Flores and Justin Kirby and Lee A. D. Cooper and Holger R. Roth and Daguang Xu and David Bericat and Ralf Floca and S. Kevin Zhou and Haris Shuaib and Keyvan Farahani and Klaus H. Maier-Hein and Stephen Aylward and Prerna Dogra and Sebastien Ourselin and Andrew Feng},
year={2022},
eprint={2211.02701},
archivePrefix={arXiv},
primaryClass={cs.LG}
}

@article{quicksilver,
  title={Quicksilver: Fast predictive image registration--a deep learning approach},
  author={Yang, Xiao and Kwitt, Roland and Styner, Martin and Niethammer, Marc},
  journal={NeuroImage},
  volume={158},
  pages={378--396},
  year={2017},
  publisher={Elsevier}
}

@InProceedings{lapirn,
author="Mok, Tony C. W.
and Chung, Albert C. S.",
editor="Martel, Anne L.
and Abolmaesumi, Purang
and Stoyanov, Danail
and Mateus, Diana
and Zuluaga, Maria A.
and Zhou, S. Kevin
and Racoceanu, Daniel
and Joskowicz, Leo",
title="Large Deformation Diffeomorphic Image Registration with {Laplacian} Pyramid Networks",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="211--221",
isbn="978-3-030-59716-0"
}


@article{oasis,
  title={{OASIS-3}: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and {Alzheimer} disease},
  author={LaMontagne, Pamela J and Benzinger, Tammie LS and Morris, John C and Keefe, Sarah and Hornbeck, Russ and Xiong, Chengjie and Grant, Elizabeth and Hassenstab, Jason and Moulder, Krista and Vlassenko, Andrei G and others},
  journal={MedRxiv},
  pages={2019--12},
  year={2019},
  publisher={Cold Spring Harbor Laboratory Press}
}

@InProceedings{oneshot-augment,
author = {Zhao, Amy and Balakrishnan, Guha and Durand, Fredo and Guttag, John V. and Dalca, Adrian V.},
title = {Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

@article{semi-augment,
title = {Semi-supervised task-driven data augmentation for medical image segmentation},
journal = {Medical Image Analysis},
volume = {68},
pages = {101934},
year = {2021},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2020.101934},
url = {https://www.sciencedirect.com/science/article/pii/S136184152030298X},
author = {Krishna Chaitanya and Neerav Karani and Christian F. Baumgartner and Ertunc Erdil and Anton Becker and Olivio Donati and Ender Konukoglu},
keywords = {Data augmentation, Semi-supervised learning, Machine learning, Deep learning, Medical image segmentation},
}

@InProceedings{auto-augment,
author="Xu, Ju
and Li, Mengzhang
and Zhu, Zhanxing",
editor="Martel, Anne L.
and Abolmaesumi, Purang
and Stoyanov, Danail
and Mateus, Diana
and Zuluaga, Maria A.
and Zhou, S. Kevin
and Racoceanu, Daniel
and Joskowicz, Leo",
title="Automatic Data Augmentation for {3D} Medical Image Segmentation",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="378--387",
isbn="978-3-030-59710-8"
}

@article{minimizelatent1,
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}

@inproceedings{minimizelatent2,
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@inproceedings{deepcoral,
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  year={2016},
  organization={Springer}
}

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}


@article{advfeature1,
  title={Domain-adversarial training of neural networks},
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  pages={2096--2030},
  year={2016},
  publisher={JMLR. org}
}

@inproceedings{advfeature2,
  title={Adversarial discriminative domain adaptation},
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  pages={7167--7176},
  year={2017}
}

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  volume={29},
  year={2016}
}


@inproceedings{translation1,
  title={Image to image translation for domain adaptation},
  author={Murez, Zak and Kolouri, Soheil and Kriegman, David and Ramamoorthi, Ravi and Kim, Kyungnam},
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  pages={4500--4509},
  year={2018}
}

@inproceedings{translation2,
  title={Domain bridge for unpaired image-to-image translation and unsupervised domain adaptation},
  author={Pizzati, Fabio and Charette, Raoul de and Zaccaria, Michela and Cerri, Pietro},
  booktitle={Proceedings of the IEEE/CVF winter conference on applications of computer vision},
  pages={2990--2998},
  year={2020}
}

@inproceedings{translation3,
  title={Model adaptation: Unsupervised domain adaptation without source data},
  author={Li, Rui and Jiao, Qianfen and Cao, Wenming and Wong, Hau-San and Wu, Si},
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  year={2020}
}

@inproceedings{xie2018learning,
  title={Learning semantic representations for unsupervised domain adaptation},
  author={Xie, Shaoan and Zheng, Zibin and Chen, Liang and Chen, Chuan},
  booktitle={International conference on machine learning},
  pages={5423--5432},
  year={2018},
  organization={PMLR}
}

@article{wang2021discriminative,
  title={Discriminative feature alignment: Improving transferability of unsupervised domain adaptation by Gaussian-guided latent alignment},
  author={Wang, Jing and Chen, Jiahong and Lin, Jianzhe and Sigal, Leonid and de Silva, Clarence W},
  journal={Pattern Recognition},
  volume={116},
  pages={107943},
  year={2021},
  publisher={Elsevier}
}

@INPROCEEDINGS{irospaper,
  author={Gogoll, Dario and Lottes, Philipp and Weyler, Jan and Petrinic, Nik and Stachniss, Cyrill},
  booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
  title={Unsupervised Domain Adaptation for Transferring Plant Classification Systems to New Field Environments, Crops, and Robots}, 
  year={2020},
  volume={},
  number={},
  pages={2636-2642},
  doi={10.1109/IROS45743.2020.9341277}
}

@article{kong2021breaking,
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  author={Kong, Lingke and Lian, Chenyu and Huang, Detian and Hu, Yanle and Zhou, Qichao and others},
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  pages={1964--1978},
  year={2021}
}

@ARTICLE{ssim,
  author={Zhou Wang and Bovik, A.C. and Sheikh, H.R. and Simoncelli, E.P.},
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@article{ukbiobank,
  title={{UK biobank}: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age},
  author={Sudlow, Cathie and Gallacher, John and Allen, Naomi and Beral, Valerie and Burton, Paul and Danesh, John and Downey, Paul and Elliott, Paul and Green, Jane and Landray, Martin and others},
  journal={PLoS medicine},
  volume={12},
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  year={2015},
  publisher={Public Library of Science}
}

@article{segmentation-loss,
  title={Weakly-supervised convolutional neural networks for multimodal image registration},
  author={Hu, Yipeng and Modat, Marc and Gibson, Eli and Li, Wenqi and Ghavami, Nooshin and Bonmati, Ester and Wang, Guotai and Bandula, Steven and Moore, Caroline M and Emberton, Mark and others},
  journal={Medical image analysis},
  volume={49},
  pages={1--13},
  year={2018},
  publisher={Elsevier}
}

@INPROCEEDINGS{semgan,
  author={Cherian, Anoop and Sullivan, Alan},
  booktitle={2019 IEEE Winter Conference on Applications of Computer Vision (WACV)}, 
  title={{Sem-GAN}: Semantically-Consistent Image-to-Image Translation}, 
  year={2019},
  volume={},
  number={},
  pages={1797-1806},
  keywords={Gallium nitride;Semantics;Image segmentation;Generative adversarial networks;Task analysis;Generators;Adaptation models},
  doi={10.1109/WACV.2019.00196}}

@InProceedings{res-unet,
author="Kerfoot, Eric
and Clough, James
and Oksuz, Ilkay
and Lee, Jack
and King, Andrew P.
and Schnabel, Julia A.",
editor="Pop, Mihaela
and Sermesant, Maxime
and Zhao, Jichao
and Li, Shuo
and McLeod, Kristin
and Young, Alistair
and Rhode, Kawal
and Mansi, Tommaso",
title="Left-Ventricle Quantification Using Residual U-Net",
booktitle="Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="371--380",
}

