@article{sitzmann2020implicit,
  title={Implicit neural representations with periodic activation functions},
  author={Sitzmann, Vincent and Martel, Julien and Bergman, Alexander and Lindell, David and Wetzstein, Gordon},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={7462--7473},
  year={2020}
}

@InProceedings{wolterink2022implicit,
  title = 	 {Implicit Neural Representations for Deformable Image Registration},
  author =       {Wolterink, Jelmer M and Zwienenberg, Jesse C and Brune, Christoph},
  booktitle = 	 {Proceedings of The 5th International Conference on Medical Imaging with Deep Learning},
  pages = 	 {1349--1359},
  year = 	 {2022},
  editor = 	 {Konukoglu, Ender and Menze, Bjoern and Venkataraman, Archana and Baumgartner, Christian and Dou, Qi and Albarqouni, Shadi},
  volume = 	 {172},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {06--08 Jul},
  publisher =    {PMLR},
}

@article{xia2021super,
  title={Super-resolution of cardiac MR cine imaging using conditional GANs and unsupervised transfer learning},
  author={Xia, Yan and Ravikumar, Nishant and Greenwood, John P and Neubauer, Stefan and Petersen, Steffen E and Frangi, Alejandro F},
  journal={Medical Image Analysis},
  volume={71},
  pages={102037},
  year={2021},
  publisher={Elsevier}
}

@article{qiao2020temporally,
  title={Temporally coherent cardiac motion tracking from cine {MRI}: Traditional registration method and modern CNN method},
  author={Qiao, Mengyun and Wang, Yuanyuan and Guo, Yi and Huang, Lu and Xia, Liming and Tao, Qian},
  journal={Medical Physics},
  volume={47},
  number={9},
  pages={4189--4198},
  year={2020},
  publisher={Wiley Online Library}
}

@article{SANDER2023107266,
title = {Reconstruction and completion of high-resolution {3D} cardiac shapes using anisotropic {CMRI} segmentations and continuous implicit neural representations},
journal = {Computers in Biology and Medicine},
pages = {107266},
year = {2023},
issn = {0010-4825},
doi = {https://doi.org/10.1016/j.compbiomed.2023.107266},
author = {Jörg Sander and Bob D. {de Vos} and Steffen Bruns and Nils Planken and Max A. Viergever and Tim Leiner and Ivana Išgum}
}

@inproceedings{wang2020deepflash,
  title={Deepflash: An efficient network for learning-based medical image registration},
  author={Wang, Jian and Zhang, Miaomiao},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={4444--4452},
  year={2020}
}

@article{bucius2020comparison,
  title={Comparison of feature tracking, fast-SENC, and myocardial tagging for global and segmental left ventricular strain},
  author={Bucius, Paulius and Erley, Jennifer and Tanacli, Radu and Zieschang, Victoria and Giusca, Sorin and Korosoglou, Grigorious and Steen, Henning and Stehning, Christian and Pieske, Burkert and Pieske-Kraigher, Elisabeth and others},
  journal={ESC heart failure},
  volume={7},
  number={2},
  pages={523--532},
  year={2020},
  publisher={Wiley Online Library}
}

@article{scatteia2017strain,
  title={Strain imaging using cardiac magnetic resonance},
  author={Scatteia, Alessandra and Baritussio, Anna and Bucciarelli-Ducci, Chiara},
  journal={Heart failure reviews},
  volume={{22}},
  pages={465--476},
  year={2017},
  publisher={Springer}
}

@article{sander2020automatic,
  title={Automatic segmentation with detection of local segmentation failures in cardiac {MRI}},
  author={Sander, J{\"o}rg and de Vos, Bob D and I{\v{s}}gum, Ivana},
  journal={Scientific Reports},
  volume={10},
  number={1},
  pages={21769},
  year={2020},
  publisher={Nature Publishing Group UK London}
}

@INPROCEEDINGS{puyol2018strain,
  author={Puyol-Antón, Esther and Ruijsink, Bram and Bai, Wenjia and Langet, Hélène and De Craene, Mathieu and Schnabel, Julia A. and Piro, Paolo and King, Andrew P. and Sinclair, Matthew},
  booktitle={2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)}, 
  title={Fully automated myocardial strain estimation from cine {MRI} using convolutional neural networks}, 
  year={2018},
  volume={},
  number={},
  pages={1139-1143},
  doi={10.1109/ISBI.2018.8363772}}

@article{morales2021deepstrain,
  title={Deep{Strain}: a deep learning workflow for the automated characterization of cardiac mechanics},
  author={Morales, Manuel A and Van den Boomen, Maaike and Nguyen, Christopher and Kalpathy-Cramer, Jayashree and Rosen, Bruce R and Stultz, Collin M and Izquierdo-Garcia, David and Catana, Ciprian},
  journal={Frontiers in Cardiovascular Medicine},
  volume={8},
  pages={730316},
  year={2021},
  publisher={Frontiers}
}

@article{zhang2021comparing,
  title={Comparing cardiovascular magnetic resonance strain software packages by their abilities to discriminate outcomes in patients with heart failure with preserved ejection fraction},
  author={Zhang, Ying and Mui, David and Chirinos, Julio A and Zamani, Payman and Ferrari, Victor A and Chen, Yucheng and Han, Yuchi},
  journal={Journal of Cardiovascular Magnetic Resonance},
  volume={23},
  pages={1--10},
  year={2021},
  publisher={Springer}
}

@article{zhu2023residual,
  title={A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning},
  author={Zhu, Jin and Yang, Guang and Lio, Pietro},
  journal={arXiv preprint arXiv:2302.11184},
  year={2023}
}

@inproceedings{georgescu2023multimodal,
  title={Multimodal multi-head convolutional attention with various kernel sizes for medical image super-resolution},
  author={Georgescu, Mariana-Iuliana and Ionescu, Radu Tudor and Miron, Andreea-Iuliana and Savencu, Olivian and Ristea, Nicolae-C{\u{a}}t{\u{a}}lin and Verga, Nicolae and Khan, Fahad Shahbaz},
  booktitle={Proceedings of the IEEE/CVF winter conference on applications of computer vision},
  pages={2195--2205},
  year={2023}
}

@article{ottesen2023densely,
  title={A densely interconnected network for deep learning accelerated MRI},
  author={Ottesen, Jon Andr{\'e} and Caan, Matthan WA and Groote, Inge Rasmus and Bj{\o}rnerud, Atle},
  journal={Magnetic Resonance Materials in Physics, Biology and Medicine},
  volume={36},
  number={1},
  pages={65--77},
  year={2023},
  publisher={Springer}
}

@article{sandino2021accelerating,
  title={Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction},
  author={Sandino, Christopher M and Lai, Peng and Vasanawala, Shreyas S and Cheng, Joseph Y},
  journal={Magnetic Resonance in Medicine},
  volume={85},
  number={1},
  pages={152--167},
  year={2021},
  publisher={Wiley Online Library}
}

@article{zhu2022accelerated,
  title={Accelerated cardiac cine MRI using spatiotemporal correlation-based hybrid plug-and-play priors (SEABUS)},
  author={Zhu, Qingyong and Liu, Bei and Cui, Zhuo-Xu and Cheng, Jing and Cao, Chentao and Liu, Yuanyuan and Liang, Dong and Zhu, Yanjie},
  journal={Physics in Medicine \& Biology},
  volume={67},
  number={21},
  pages={215008},
  year={2022},
  publisher={IOP Publishing}
}

@article{feng2013highly,
  title={Highly accelerated real-time cardiac cine MRI using k--t SPARSE-SENSE},
  author={Feng, Li and Srichai, Monvadi B and Lim, Ruth P and Harrison, Alexis and King, Wilson and Adluru, Ganesh and Dibella, Edward VR and Sodickson, Daniel K and Otazo, Ricardo and Kim, Daniel},
  journal={Magnetic resonance in medicine},
  volume={70},
  number={1},
  pages={64--74},
  year={2013},
  publisher={Wiley Online Library}
}

@article{zhang2023camp,
  title={CAMP-Net: Context-Aware Multi-Prior Network for Accelerated MRI Reconstruction},
  author={Zhang, Liping and Li, Xiaobo and Chen, Weitian},
  journal={arXiv preprint arXiv:2306.11238},
  year={2023}
}

@article{heermann2014biventricular,
  title={Biventricular myocardial strain analysis in patients with arrhythmogenic right ventricular cardiomyopathy ({ARVC}) using cardiovascular magnetic resonance feature tracking},
  author={Heermann, Philipp and Hedderich, Dennis M and Paul, Matthias and Sch{\"u}lke, Christoph and Kroeger, Jan Robert and Bae{\ss}ler, Bettina and Wichter, Thomas and Maintz, David and Waltenberger, Johannes and Heindel, Walter and others},
  journal={Journal of Cardiovascular Magnetic Resonance},
  doi={https://doi.org/10.1186/s12968-014-0075-z},
  volume={16},
  number={1},
  pages={1--13},
  year={2014},
  publisher={BioMed Central}
}



@inproceedings{de2020mutual,
  title={Mutual information for unsupervised deep learning image registration},
  author={de Vos, Bob D and van der Velden, Bas HM and Sander, J{\"o}rg and Gilhuijs, Kenneth GA and Staring, Marius and I{\v{s}}gum, Ivana},
  booktitle={Medical Imaging 2020: Image Processing},
  volume={11313},
  pages={155--161},
  year={2020},
  organization={SPIE}
}

@article{upendra2021motion, 
year = {2021}, 
title = {{Motion Extraction of the Right Ventricle from 4D Cardiac Cine MRI Using A Deep Learning-Based Deformable Registration Framework}}, 
author = {Upendra, Roshan Reddy and Hasan, S. M. Kamrul and Simon, Richard and Wentz, Brian Jamison and Shontz, Suzanne M. and Sacks, Michael S. and Linte, Cristian A.}, 
journal = {2021 43rd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)}, 
doi = {10.1109/embc46164.2021.9630586}, 
pmid = {34892062}, 
pmcid = {PMC9137928}, 
pages = {3795--3799}, 
keywords = {}, 
}
@inproceedings{glorot2010understanding,
  title={Understanding the difficulty of training deep feedforward neural networks},
  author={Glorot, Xavier and Bengio, Yoshua},
  booktitle={Proceedings of the thirteenth international conference on artificial intelligence and statistics},
  pages={249--256},
  year={2010},
  organization={JMLR Workshop and Conference Proceedings}
}

@article{meng2022mulvimotion, 
year = {2022}, 
title = {{MulViMotion: Shape-Aware 3D Myocardial Motion Tracking From Multi-View Cardiac MRI}}, 
author = {Meng, Qingjie and Qin, Chen and Bai, Wenjia and Liu, Tianrui and Marvao, Antonio de and O'Regan, Declan P and Rueckert, Daniel and Meng, Qingjie}, 
journal = {IEEE Transactions on Medical Imaging}, 
issn = {0278-0062}, 
doi = {10.1109/tmi.2022.3154599}, 
pmid = {35201985}, 
pmcid = {PMC7613225}, 
pages = {1961--1974}, 
number = {8}, 
volume = {41}, 
keywords = {},
}


@article{qin2023generative, 
year = {2023}, 
rating = {5}, 
title = {{Generative myocardial motion tracking via latent space exploration with biomechanics-informed prior}}, 
author = {Qin, Chen and Wang, Shuo and Chen, Chen and Bai, Wenjia and Rueckert, Daniel}, 
journal = {Medical Image Analysis}, 
issn = {1361-8415}, 
doi = {10.1016/j.media.2022.102682}, 
pmid = {36403311}, 
pages = {102682}, 
volume = {83}, 
keywords = {}, 
}

@article{SanderAutoencoder, 
year = {2022}, 
keywords = {Super-Resolution}, 
title = {{Autoencoding low-resolution MRI for semantically smooth interpolation of anisotropic MRI}}, 
author = {Sander, Jörg and Vos, Bob D. de and Išgum, Ivana}, 
journal = {Medical Image Analysis}, 
issn = {1361-8415}, 
doi = {10.1016/j.media.2022.102393}, 
pmid = {35228070}, 
eprint = {2202.09258}, 
pages = {102393}, 
volume = {78}, 
}



# INTRODUCTION

@article{lange2021quantification,
  title={Quantification of myocardial deformation applying CMR-feature-tracking—All about the left ventricle?},
  author={Lange, Torben and Schuster, Andreas},
  journal={Current Heart Failure Reports},
  volume={18},
  number={4},
  pages={225--239},
  year={2021},
  publisher={Springer}
}

@InProceedings{alvarez2023,
author="Alvarez-Florez, Laura
and Sander, J{\"o}rg
and Bourfiss, Mimount
and Tjong, Fleur V. Y.
and Velthuis, Birgitta K.
and I{\v{s}}gum, Ivana",
editor="Camara, Oscar
and Puyol-Ant{\'o}n, Esther
and Sermesant, Maxime
and Suinesiaputra, Avan
and Tao, Qian
and Wang, Chengyan
and Young, Alistair",
title="Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy",
booktitle="Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="25--34",
isbn="978-3-031-52448-6"
}

@article{adam2014method,
  title={A method for stochastic optimization},
  author={Diederik P. Kingma and Jimmy Ba},
  journal={arXiv preprint arXiv:1412.6980},
  volume={1412},
  number={6},
  year={2014}
}

@InProceedings{pmlr-v172-wolterink22a,
  title = 	 {Implicit Neural Representations for Deformable Image Registration},
  author =       {Wolterink, Jelmer M and Zwienenberg, Jesse C and Brune, Christoph},
  booktitle = 	 {Proceedings of The 5th International Conference on Medical Imaging with Deep Learning},
  pages = 	 {1349--1359},
  year = 	 {2022},
  editor = 	 {Konukoglu, Ender and Menze, Bjoern and Venkataraman, Archana and Baumgartner, Christian and Dou, Qi and Albarqouni, Shadi},
  volume = 	 {172},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {06--08 Jul},
  publisher =    {PMLR},
}


@article{lopez2023warppinn,
  title={WarpPINN: Cine-MR image registration with physics-informed neural networks},
  author={L{\'o}pez, Pablo Arratia and Mella, Hern{\'a}n and Uribe, Sergio and Hurtado, Daniel E and Costabal, Francisco Sahli},
  journal={Medical Image Analysis},
  volume={89},
  pages={102925},
  year={2023},
  publisher={Elsevier}
}

@article{leenders2011septal,
  title={Septal Deformation Patterns Delineate Mechanical Dyssynchrony and Regional Differences in Contractility},
  author={Leenders, Geert E and Lumens, Joost and Cramer, Maarten J and De Boeck, Bart WL and Doevendans, Pieter A and Delhaas, Tammo and Prinzen, Frits W},
  year={2011}
}

@article{morales2023comparison,
  title={Comparison of DeepStrain and feature tracking for cardiac MRI strain analysis},
  author={Morales, Manuel A and Cirillo, Julia and Nakata, Kei and Kucukseymen, Selcuk and Ngo, Long H and Izquierdo-Garcia, David and Catana, Ciprian and Nezafat, Reza},
  journal={Journal of Magnetic Resonance Imaging},
  volume={57},
  number={5},
  pages={1507--1515},
  year={2023},
  publisher={Wiley Online Library}
}


@article{ibrahim2011myocardial,
  title={Myocardial tagging by cardiovascular magnetic resonance: evolution of techniques--pulse sequences, analysis algorithms, and applications},
  author={Ibrahim, El-Sayed H},
  journal={Journal of Cardiovascular Magnetic Resonance},
  volume={13},
  number={1},
  pages={36},
  year={2011},
  publisher={Elsevier}
}


@article{fudim2021mechanical,
  title={Mechanical dyssynchrony: How do we measure it, what it means, and what we can do about it},
  author={Fudim, Marat and Dalgaard, Frederik and Fathallah, Mouhammad and Iskandrian, Ami E and Borges-Neto, Salvator},
  journal={Journal of Nuclear Cardiology},
  volume={28},
  number={5},
  pages={2174--2184},
  year={2021},
  publisher={Springer}
}


# RELATED WORK


@inproceedings{upendra2020convolutional,
  title={A convolutional neural network-based deformable image registration method for cardiac motion estimation from cine cardiac MR images},
  author={Upendra, Roshan Reddy and Wentz, Brian Jamison and Shontz, Suzanne M and Linte, Cristian A},
  booktitle={2020 Computing in Cardiology},
  pages={1--4},
  year={2020},
  organization={IEEE}
}


@inproceedings{qin2020biomechanics,
  title={Biomechanics-informed neural networks for myocardial motion tracking in MRI},
  author={Qin, Chen and Wang, Shuo and Chen, Chen and Qiu, Huaqi and Bai, Wenjia and Rueckert, Daniel},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={296--306},
  year={2020},
  organization={Springer}
}


@article{de2019deep,
  title={A deep learning framework for unsupervised affine and deformable image registration},
  author={De Vos, Bob D and Berendsen, Floris F and Viergever, Max A and Sokooti, Hessam and Staring, Marius and I{\v{s}}gum, Ivana},
  journal={Medical image analysis},
  volume={52},
  pages={128--143},
  year={2019},
  publisher={Elsevier}
}


@article{acdc,
  title={Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?},
  author={Bernard, Olivier and Lalande, Alain and Zotti, Clement and Cervenansky, Frederick and Yang, Xin and Heng, Pheng-Ann and Cetin, Irem and Lekadir, Karim and Camara, Oscar and Ballester, Miguel Angel Gonzalez and others},
  journal={IEEE transactions on medical imaging},
  volume={37},
  number={11},
  pages={2514--2525},
  year={2018},
  publisher={ieee}
}


@article{mms2,
  title={Deep learning segmentation of the right ventricle in cardiac MRI: the M\&Ms challenge},
  author={Mart{\'\i}n-Isla, Carlos and Campello, V{\'\i}ctor M and Izquierdo, Cristian and Kushibar, Kaisar and Sendra-Balcells, Carla and Gkontra, Polyxeni and Sojoudi, Alireza and Fulton, Mitchell J and Arega, Tewodros Weldebirhan and Punithakumar, Kumaradevan and others},
  journal={IEEE Journal of Biomedical and Health Informatics},
  volume={27},
  number={7},
  pages={3302--3313},
  year={2023},
  publisher={IEEE}
}


@article{xue2022regional,
  title={Regional cardiac motion scoring with multi-scale motion-based spatial attention},
  author={Xue, Wufeng and Chen, Zejian and Wang, Tianfu and Li, Shuo and Ni, Dong},
  journal={IEEE Journal of Biomedical and Health Informatics},
  volume={26},
  number={7},
  pages={3116--3126},
  year={2022},
  publisher={IEEE}
}

@article{bjerregaard2023strain,
  title={Strain imaging and ventricular arrhythmia},
  author={Bjerregaard, Caroline L{\o}kke and Skaarup, Kristoffer Grundtvig and Lassen, Mats Christian H{\o}jbjerg and Biering-S{\o}rensen, Tor and Olsen, Flemming Javier},
  journal={Diagnostics},
  volume={13},
  number={10},
  pages={1778},
  year={2023},
  publisher={MDPI}
}



@article{american2002standardized,
  title={Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: a statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association},
  author={American Heart Association Writing Group on Myocardial Segmentation and Registration for Cardiac Imaging: and Cerqueira, Manuel D and Weissman, Neil J and Dilsizian, Vasken and Jacobs, Alice K and Kaul, Sanjiv and Laskey, Warren K and Pennell, Dudley J and Rumberger, John A and Ryan, Thomas and others},
  journal={Circulation},
  volume={105},
  number={4},
  pages={539--542},
  year={2002},
  publisher={Lippincott Williams \& Wilkins}
}



% MAS

@misc{who_cvd_2021,
  author       = {{World Health Organization}},
  title        = {Cardiovascular diseases (CVDs) — Fact sheet},
  howpublished = {\url{https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)}},
  year         = {2021},
  month        = {June},
  note         = {Accessed 4 July 2025},
  institution  = {World Health Organization}
}


@article{poterucha2025detecting,
  title={Detecting structural heart disease from electrocardiograms using AI},
  author={Poterucha, Timothy J and Jing, Linyuan and Ricart, Ramon Pimentel and Adjei-Mosi, Michael and Finer, Joshua and Hartzel, Dustin and Kelsey, Christopher and Long, Aaron and Rocha, Daniel and Ruhl, Jeffrey A and others},
  journal={Nature},
  pages={1--10},
  year={2025},
  publisher={Nature Publishing Group UK London}
}

@article{wang2025tac,
  title={TAC-ECG: A task-adaptive classification method for electrocardiogram based on cross-modal contrastive learning and low-rank convolutional adapter},
  author={Wang, Rongjia and Dong, Xunde and Liu, Xiuling and Fang, Yihai and Dou, Jianhong and Qiang, Yupeng and Yang, Yang and Hu, Fei},
  journal={Computer Methods and Programs in Biomedicine},
  pages={108918},
  year={2025},
  publisher={Elsevier}
}


@article{hendrycks2016gaussian,
  title={Gaussian error linear units (gelus)},
  author={Hendrycks, Dan and Gimpel, Kevin},
  journal={arXiv preprint arXiv:1606.08415},
  year={2016}
}





@article{dosovitskiy2020image,
  title={An image is worth 16x16 words: Transformers for image recognition at scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and others},
  journal={arXiv preprint arXiv:2010.11929},
  year={2020}
}

@article{di2024heart,
  title={The heart of the world},
  author={Di Cesare, Mariachiara and Perel, Pablo and Taylor, Sean and Kabudula, Chodziwadziwa and Bixby, Honor and Gaziano, Thomas A and McGhie, Diana Vaca and Mwangi, Jeremiah and Pervan, Borjana and Narula, Jagat and others},
  journal={Global heart},
  volume={19},
  number={1},
  pages={11},
  year={2024}
}


@incollection{rnmo2006electrocardiogram,
  title={Electrocardiogram (ECG) signal processing},
  author={Rnmo, Leif So and Laguna, Pablo},
  booktitle={Wiley Encyclopedia Biomed. Eng.},
  pages={1--16},
  year={2006}
}

@article{liu2021deep,
  title={Deep learning in ECG diagnosis: A review},
  author={Liu, Xinwen and Wang, Huan and Li, Zongjin and Qin, Lang},
  journal={Knowledge-Based Systems},
  volume={227},
  pages={107187},
  year={2021},
  publisher={Elsevier}
}


@inproceedings{wortsman2022model,
  title={Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time},
  author={Wortsman, Mitchell and Ilharco, Gabriel and Gadre, Samir Ya and Roelofs, Rebecca and Gontijo-Lopes, Raphael and Morcos, Ari S and Namkoong, Hongseok and Farhadi, Ali and Carmon, Yair and Kornblith, Simon and others},
  booktitle={International conference on machine learning},
  pages={23965--23998},
  year={2022},
  organization={PMLR}
}
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@article{Turgut2025,
   author = {Özgün Turgut and Philip Müller and Paul Hager and Suprosanna Shit and Sophie Starck and Martin J Menten and Eimo Martens and Daniel Rueckert},
   doi = {https://doi.org/10.1016/j.media.2024.103451},
   issn = {1361-8415},
   journal = {Medical Image Analysis},
   keywords = {Cardiac magnetic resonance imaging,Electrocardiogram,Masked data modelling,Multimodal contrastive learning,Self-supervised learning},
   pages = {103451},
   title = {Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging},
   volume = {101},
   year = {2025}
}



@inproceedings{DingZhengyaoandHu2024,
  title={Cross-modality cardiac insight transfer: A contrastive learning approach to enrich ecg with cmr features},
  author={Ding, Zhengyao and Hu, Yujian and Li, Ziyu and Zhang, Hongkun and Wu, Fei and Xiang, Yilang and Li, Tian and Liu, Ziyi and Chu, Xuesen and Huang, Zhengxing},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={109--119},
  year={2024},
  organization={Springer}
}

@article{petersen2016uk,
  title={UK Biobank's cardiovascular magnetic resonance protocol},
  author={Petersen, Steffen E and Matthews, Paul M and Francis, Jane M and Robson, Matthew D and Zemrak, Filip and Boubertakh, Redha and Young, Alistair A and Hudson, Sarah and Weale, Peter and Garratt, Steve and others},
  journal={Journal of cardiovascular magnetic resonance},
  volume={18},
  number={1},
  pages={8},
  year={2016},
  publisher={Elsevier}
}

@inproceedings{radford2021learningtransferablevisualmodels,
  title={Learning transferable visual models from natural language supervision},
  author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
  booktitle={International conference on machine learning},
  pages={8748--8763},
  year={2021},
  organization={PmLR}
}



@article{turgut2025unlocking,
  title={Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging},
  author={Turgut, {\"O}zg{\"u}n and M{\"u}ller, Philip and Hager, Paul and Shit, Suprosanna and Starck, Sophie and Menten, Martin J and Martens, Eimo and Rueckert, Daniel},
  journal={Medical Image Analysis},
  volume={101},
  pages={103451},
  year={2025},
  publisher={Elsevier}
}


@misc{chen2020SimCLRCL,
      title={A Simple Framework for Contrastive Learning of Visual Representations}, 
      author={Ting Chen and Simon Kornblith and Mohammad Norouzi and Geoffrey Hinton},
      year={2020},
      eprint={2002.05709},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2002.05709}, 
}


@article{loshchilov2019decoupledweightdecayregularizationAdamW,
  title={Decoupled weight decay regularization},
  author={Loshchilov, Ilya and Hutter, Frank},
  journal={arXiv preprint arXiv:1711.05101},
  year={2017}
}


@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}



@InProceedings{He_2022_CVPR,
    author    = {He, Kaiming and Chen, Xinlei and Xie, Saining and Li, Yanghao and Doll\'ar, Piotr and Girshick, Ross},
    title     = {Masked Autoencoders Are Scalable Vision Learners},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {16000-16009}
}




@article{simonyan2015deepconvolutionalnetworkslargescale,
  title={Very deep convolutional networks for large-scale image recognition},
  author={Simonyan, Karen and Zisserman, Andrew},
  journal={arXiv preprint arXiv:1409.1556},
  year={2014}
}


@article{Radhakrishnan2023,
   abstract = {A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results systematically integrate distinct diagnostic modalities into a common representation that better characterizes physiologic state.},
   author = {Adityanarayanan Radhakrishnan and Sam F Friedman and Shaan Khurshid and Kenney Ng and Puneet Batra and Steven A Lubitz and Anthony A Philippakis and Caroline Uhler},
   doi = {10.1038/s41467-023-38125-0},
   issn = {2041-1723},
   issue = {1},
   journal = {Nature Communications},
   pages = {2436},
   title = {Cross-modal autoencoder framework learns holistic representations of cardiovascular state},
   volume = {14},
   year = {2023}
}


@misc{feichtenhofer2019slowfastnetworksvideorecognitionRESNET503D,
      title={SlowFast Networks for Video Recognition}, 
      author={Christoph Feichtenhofer and Haoqi Fan and Jitendra Malik and Kaiming He},
      year={2019},
      eprint={1812.03982},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1812.03982}, 
}


@article{zhang2024improving,
  title={Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical translation},
  author={Zhang, Qiang and Fotaki, Anastasia and Ghadimi, Sona and Wang, Yu and Doneva, Mariya and Wetzl, Jens and Delfino, Jana G and O’Regan, Declan P and Prieto, Claudia and Epstein, Frederick H},
  journal={Journal of Cardiovascular Magnetic Resonance},
  volume={26},
  number={2},
  pages={101051},
  year={2024},
  publisher={Elsevier}
}

@inproceedings{koneputugodage2025vi,
  title={VI\^{} 3NR: Variance Informed Initialization for Implicit Neural Representations},
  author={Koneputugodage, Chamin Hewa and Ben-Shabat, Yizhak and Ramasinghe, Sameera and Gould, Stephen},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  pages={13477--13486},
  year={2025}
}