@article{huang2020fusion,title={Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines},author={Huang, Shih-Cheng and Pareek, Anuj and Seyyedi, Saeed and Banerjee, Imon and Lungren, Matthew P},journal={NPJ Digital Medicine},volume={3},number={1},pages={1--9},year={2020},publisher={Nature Publishing Group}

}@article{acosta2022multimodal,title={Multimodal biomedical {AI}},author={Acosta, Juan N and Falcone, Guido J and Rajpurkar, Pranav and Topol, Eric J},journal={Nature Medicine},volume={28},number={9},pages={1773--1784},year={2022},publisher={Nature Publishing Group}}

@InProceedings{pmlr-v182-zhang22a,
  title     = {Contrastive Learning of Medical Visual Representations from Paired Images and Text},
  author    = {Zhang, Yuhao and Jiang, Hang and Miura, Yasuhide and Manning, Christopher D. and Langlotz, Curtis P.},
  booktitle = {Proceedings of the 7th Machine Learning for Healthcare Conference},
  pages     = {2--25},
  year      = {2022},
  editor    = {Lipton, Zachary and Ranganath, Rajesh and Sendak, Mark and Sjoding, Michael and Yeung, Serena},
  volume    = {182},
  series    = {Proceedings of Machine Learning Research},
  month     = {05--06 Aug},
  publisher = {PMLR},
  url       = {https://proceedings.mlr.press/v182/zhang22a.html},
}

@inproceedings{boecking2022making,title={Making the most of text semantics to improve biomedical vision-language processing},author={Boecking, Benedikt and Usuyama, Naoto and Bannur, Shruthi and Castro, Daniel C and Schwaighofer, Anton and Hyland, Stephanie and Wetscherek, Maria and Naumann, Tristan and Nori, Aditya and Alvarez-Valle, Javier and others},booktitle={European Conference on Computer Vision},pages={1--21},year={2022},organization={Springer}}

@inproceedings{mcmahan2017communication,title={Communication-efficient learning of deep networks from decentralized data},author={McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and y Arcas, Blaise Aguera},booktitle={Artificial Intelligence and Statistics},pages={1273--1282},year={2017},organization={PMLR}}

@article{rieke2020future,title={The future of digital health with federated learning},author={Rieke, Nicola and Hancox, Jonny and Li, Wenqi and Milletar{`\i}, Fausto and Roth, Holger R and Albarqouni, Shadi and Bakas, Spyridon and Galtier, Mathieu N and Landman, Bennett A and Maier-Hein, Klaus and others},journal={NPJ Digital Medicine},volume={3},number={1},pages={1--7},year={2020},publisher={Nature Publishing Group}}

@article{sheller2020federated,title={Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data},author={Sheller, Micah J and Edwards, Brandon and Reina, G Anthony and Martin, Jason and Pati, Sarthak and Kotrotsou, Aikaterini and Milchenko, Mikhail and Xu, Weilin and Marcus, Daniel and Colen, Rivka R and others},journal={Scientific Reports},volume={10},number={1},pages={1--12},year={2020},publisher={Nature Publishing Group}}

@article{rajpurkar2017chexnet,title={CheXNet: Radiologist-level pneumonia detection on chest x-rays with deep learning},author={Rajpurkar, Pranav and Irvin, Jeremy and Zhu, Kaylie and Yang, Brandon and Mehta, Hershel and Duan, Tony and Ding, Daisy and Bagul, Aarti and Langlotz, Curtis and Shpanskaya, Katie and others},journal={arXiv preprint arXiv:1711.05225},year={2017}}

@article{warnat2021swarm,title={Swarm learning for decentralized and confidential clinical machine learning},author={Warnat-Herresthal, Stefanie and Schultze, Hartmut and Shastry, Krishnaprasad L and Manamohan, Satyanarayana and Mukherjee, Saikat and Garg, Vishesh and Bhatia, Ravi and Hildebrand, Hermann and Akogwu, Oisin and Decher, Nariman and others},journal={Nature},volume={594},number={7862},pages={265--270},year={2021},publisher={Nature Publishing Group}}

@inproceedings{ngiam2011multimodal,title={Multimodal deep learning},author={Ngiam, Jiquan and Khosla, Aditya and Kim, Mingyu and Nam, Juhan and Lee, Honglak and Ng, Andrew Y},booktitle={Proceedings of the 28th International Conference on Machine Learning (ICML-11)},pages={689--696},year={2011}}

@inproceedings{ma2021smil,title={Smil: Multimodal learning with severely missing modality},author={Ma, Mengmeng and Ren, Jian and Zhao, Long and Testuggine, Davide and Peng, Xi},booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},volume={35},number={3},pages={2302--2310},year={2021}}

@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{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}}


@misc{fda2024uq,title={Artificial Intelligence and Machine Learning ({AI/ML}) in Medical Devices},author={{U.S. Food and Drug Administration}},year={2024},howpublished={\url{https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device}},note={Accessed: 2024}}

@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},volume={30},year={2017}}

@inproceedings{seitzer2022pitfalls,title={On the pitfalls of heteroscedastic uncertainty estimation with probabilistic neural networks},author={Seitzer, Maximilian and Tavakoli, Arash and Antic, Dimitrije and Caccamo, Marco},booktitle={International Conference on Learning Representations},year={2022}}

@inproceedings{irvin2019chexpert,title={CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison},author={Irvin, Jeremy and Rajpurkar, Pranav and Ko, Michael and Yu, Yifan and Ciurea-Ilcus, Silviana and Chute, Chris and Marklund, Henrik and Haghgoo, Behzad and Ball, Robyn and Shpanskaya, Katie and others},booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},volume={33},number={01},pages={590--597},year={2019}}

@article{bustos2020padchest,title={PadChest: A large chest x-ray image dataset with multi-label annotated reports},author={Bustos, Aurelia and Pertusa, Antonio and Salinas, Jose-Maria and de la Iglesia-Vay{'a}, Maria},journal={Medical Image Analysis},volume={66},pages={101797},year={2020},publisher={Elsevier}}

@article{kaissis2020secure,title={Secure, privacy-preserving and federated machine learning in medical imaging},author={Kaissis, Georgios A and Makowski, Marcus R and R{"u}ckert, Daniel and Braren, Rickmer F},journal={Nature Machine Intelligence},volume={2},number={6},pages={305--311},year={2020},publisher={Nature Publishing Group}}

@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{laves2020wellcalibrated,title={Well-calibrated regression uncertainty in medical imaging with deep learning},author={Laves, Max-Heinrich and Ihler, Sontje and Kortmann, Karl-Philipp and Ortmaier, Tobias},booktitle={Medical Imaging with Deep Learning},pages={393--412},year={2020},organization={PMLR}}

@inproceedings{geifman2019selectivepred,title={Selective prediction for deep neural networks},author={Geifman, Yonatan and El-Yaniv, Ran},booktitle={International Conference on Machine Learning},pages={2156--2165},year={2019},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{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{devlin2019bert,title={{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding},author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},booktitle={Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},pages={4171--4186},year={2019}}

@inproceedings{li2020fedprox,title={Federated optimization in heterogeneous networks},author={Li, Tian and Sahu, Anit Kumar and Zaheer, Manzil and Sanjabi, Maziar and Talwalkar, Ameet and Smith, Virginia},booktitle={Proceedings of Machine Learning and Systems},volume={2},pages={429--450},year={2020}}

@article{dayan2021federated,title={Federated learning for predicting clinical outcomes in patients with {COVID-19}},author={Dayan, Ittai and Roth, Holger R and Zhong, Aiham and Harouni, Ahmed and Gentili, Amilcare and Abidin, Abb Z and Liu, Andrew and Costa, Anthony B and Wood, Bradford J and Tsai, Chia-Sheng and others},journal={Nature Medicine},volume={27},number={10},pages={1735--1743},year={2021},publisher={Nature Publishing Group}}

@InProceedings{Lee_2018_ECCV,
  author    = {Lee, Kuang-Huei and Chen, Xi and Hua, Gang and Hu, Houdong and He, Xiaodong},
  title     = {Stacked Cross Attention for Image-Text Matching},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  month     = {September},
  year      = {2018}
}

@article{Arevalo2017GatedMU,
  title   = {Gated Multimodal Units for Information Fusion},
  author  = {John Arevalo and Thamar Solorio and Manuel Montes-y-G{\'o}mez and Fabio A. Gonz{\'a}lez},
  journal = {ArXiv},
  year    = {2017},
  volume  = {abs/1702.01992}
}

@inproceedings{NIPS2017_3f5ee243,
  author    = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, \L ukasz and Polosukhin, Illia},
  booktitle = {Advances in Neural Information Processing Systems},
  editor    = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
  publisher = {Curran Associates, Inc.},
  title     = {Attention is All you Need},
  volume    = {30},
  year      = {2017}
}

@article{Price2019Privacy,
  title        = {Privacy in the age of medical big data},
  author       = {Price, W. Nicholson II and Cohen, I. Glenn},
  journal      = {Nature Medicine},
  volume       = {25},
  number       = {1},
  pages        = {37--43},
  year         = {2019},
  month        = jan,
  doi          = {10.1038/s41591-018-0272-7},
  pmid         = {30617331},
  issn         = {1546-170X},
  abstract     = {Big data has become the ubiquitous watch word of medical innovation. The rapid development of machine-learning techniques and artificial intelligence in particular has promised to revolutionize medical practice from the allocation of resources to the diagnosis of complex diseases. But with big data comes big risks and challenges, among them significant questions about patient privacy. Here, we outline the legal and ethical challenges big data brings to patient privacy. We discuss, among other topics, how best to conceive of health privacy; the importance of equity, consent, and patient governance in data collection; discrimination in data uses; and how to handle data breaches. We close by sketching possible ways forward for the regulatory system.},
  publisher    = {Nature Publishing Group}
}


@article{Teoh2024MultimodalFusion,
  title        = {Advancing healthcare through multimodal data fusion: a comprehensive review of techniques and applications},
  author       = {Teoh, Jing Ru and Dong, Jian and Zuo, Xiaowei and Lai, Khin Wee and Hasikin, Khairunnisa and Wu, Xiang},
  journal      = {PeerJ Computer Science},
  volume       = {10},
  pages        = {e2298},
  year         = {2024},
  doi          = {10.7717/peerj-cs.2298},
  pmid         = {39650483},
  issn         = {2376-5992},
  abstract     = {With the increasing availability of diverse healthcare data sources, such as medical images and electronic health records, there is a growing need to effectively integrate and fuse this multimodal data for comprehensive analysis and decision-making. However, despite its potential, multimodal data fusion in healthcare remains limited. This review paper provides an overview of existing literature on multimodal data fusion in healthcare, covering 69 relevant works published between 2018 and 2024. It focuses on methodologies that integrate different data types to enhance medical analysis, including techniques for integrating medical images with structured and unstructured data, combining multiple image modalities, and other features. Additionally, the paper reviews various approaches to multimodal data fusion, such as early, intermediate, and late fusion methods, and examines the challenges and limitations associated with these techniques. The potential benefits and applications of multimodal data fusion in various diseases are highlighted, illustrating specific strategies employed in healthcare artificial intelligence (AI) model development. This research synthesizes existing information to facilitate progress in using multimodal data for improved medical diagnosis and treatment planning.},
  publisher    = {PeerJ},
  note         = {eCollection 2024}
}

@inproceedings{wang2017chestxray8,
  title     = {ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases},
  author    = {Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi and Summers, Ronald M},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages     = {3462--3471},
  year      = {2017},
  howpublished = {\url{https://nihcc.app.box.com/v/ChestXray-NIHCC}}
}

@misc{poudel2025multimodal,
      title={Multimodal Federated Learning With Missing Modalities through Feature Imputation Network}, 
      author={Pranav Poudel and Aavash Chhetri and Prashnna Gyawali and Georgios Leontidis and Binod Bhattarai},
      year={2025},
      eprint={2505.20232},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2505.20232}, 
}

@inproceedings{NIPS2017_9ef2ed4b,
  author    = {Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
  title     = {Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles},
  booktitle = {Advances in Neural Information Processing Systems},
  volume    = {30},
  publisher = {Curran Associates, Inc.},
  year      = {2017},
  url       = {https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf},
}

@article{melba:2021:008:laves,
  title   = {Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging},
  author  = {Laves, Max-Heinrich and Ihler, Sontje and Fast, Jacob F. and Kahrs, L{\"u}der A. and Ortmaier, Tobias},
  journal = {Machine Learning for Biomedical Imaging},
  volume  = {1},
  year    = {2021},
  pages   = {1--26},
  doi     = {10.59275/j.melba.2021-a6fd},
  url     = {https://melba-journal.org/2021:008},
}