@article{mnih2013playing,
  title={Playing atari with deep reinforcement learning},
  author={Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Graves, Alex and Antonoglou, Ioannis and Wierstra, Daan and Riedmiller, Martin},
  journal={arXiv preprint arXiv:1312.5602},
  year={2013}
}


@inproceedings{alansary2018automatic,
  title={Automatic view planning with multi-scale deep reinforcement learning agents},
  author={Alansary, Amir and Le Folgoc, Loic and Vaillant, Ghislain and Oktay, Ozan and Li, Yuanwei and Bai, Wenjia and Passerat-Palmbach, Jonathan and Guerrero, Ricardo and Kamnitsas, Konstantinos and Hou, Benjamin and others},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={277--285},
  year={2018},
  organization={Springer}
}

@inproceedings{vlontzos2019multiple,
  title={Multiple Landmark Detection using Multi-Agent Reinforcement Learning},
  author={Vlontzos, Athanasios and Alansary, Amir and Kamnitsas, Konstantinos and Rueckert, Daniel and Kainz, Bernhard},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={262--270},
  year={2019},
  organization={Springer}
}

@inproceedings{parekh2020multitask,
  title={Multitask radiological modality invariant landmark localization using deep reinforcement learning},
  author={Parekh, Vishwa S and Braverman, Vladimir and Jacobs, Michael A and others},
  booktitle={Medical Imaging with Deep Learning},
  pages={588--600},
  year={2020},
  organization={PMLR}
}

@article{menze2014multimodal,
  title={The multimodal brain tumor image segmentation benchmark (BRATS)},
  author={Menze, Bjoern H and Jakab, Andras and Bauer, Stefan and Kalpathy-Cramer, Jayashree and Farahani, Keyvan and Kirby, Justin and Burren, Yuliya and Porz, Nicole and Slotboom, Johannes and Wiest, Roland and others},
  journal={IEEE transactions on medical imaging},
  volume={34},
  number={10},
  pages={1993--2024},
  year={2014},
  publisher={IEEE}
}

@misc {PPR:PPR463670,
	Title = {A Novel Decentralized Federated Learning Approach to Train on Globally Distributed, Poor Quality, and Protected Private Medical Data},
	Author = {Nguyen, TV and Dakka, MA and Diakiw, SM and VerMilyea, MD and Perugini, M and Hall, JMM and Perugini, D},
	DOI = {10.21203/rs.3.rs-1371143/v1},
	Abstract = {Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centralized for AI training. Data-centric, cross-silo federated learning represents a pathway forward for training on distributed medical datasets. Existing approaches typically require updates to a training model to be transferred to a central server, potentially breaching data privacy laws unless the updates are sufficiently disguised or abstracted to prevent reconstruction of the dataset. Here we present a completely decentralized federated learning approach, using knowledge distillation, ensuring data privacy and protection. Each node operates independently without needing to access external data. AI accuracy using this approach is found to be comparable to centralized training, and when nodes comprise poor-quality data, which is common in healthcare, AI accuracy can exceed the performance of traditional centralized training.},
	Publisher = {Research Square},
	Year = {2022},
	URL = {https://doi.org/10.21203/rs.3.rs-1371143/v1},
}

@Article{electronics11101548,
AUTHOR = {Wang, Zhao and Hu, Yifan and Yan, Shiyang and Wang, Zhihao and Hou, Ruijie and Wu, Chao},
TITLE = {Efficient Ring-Topology Decentralized Federated Learning with Deep Generative Models for Medical Data in eHealthcare Systems},
JOURNAL = {Electronics},
VOLUME = {11},
YEAR = {2022},
NUMBER = {10},
ARTICLE-NUMBER = {1548},
URL = {https://www.mdpi.com/2079-9292/11/10/1548},
ISSN = {2079-9292},
ABSTRACT = {By leveraging deep learning technologies, data-driven-based approaches have reached great success with the rapid increase of data generated for medical applications. However, security and privacy concerns are obstacles for data providers in many sensitive data-driven scenarios, such as rehabilitation and 24 h on-the-go healthcare services. Although many federated learning (FL) approaches have been proposed with DNNs for medical applications, these works still suffer from low usability of data due to data incompleteness, low quality, insufficient quantity, sensitivity, etc. Therefore, we propose a ring-topology-based decentralized federated learning (RDFL) scheme for deep generative models (DGM), where DGM is a promising solution for solving the aforementioned data usability issues. Our RDFL schemes provide communication efficiency and maintain training performance to boost DGMs in target tasks compared with existing FL works. A novel ring FL topology and a map-reduce-based synchronizing method are designed in the proposed RDFL to improve the decentralized FL performance and bandwidth utilization. In addition, an inter-planetary file system (IPFS) is introduced to further improve communication efficiency and FL security. Extensive experiments have been taken to demonstrate the superiority of RDFL with either independent and identically distributed (IID) datasets or non-independent and identically distributed (Non-IID) datasets.},
DOI = {10.3390/electronics11101548}
}


@article{Roy2019BrainTorrentAP,
  title={BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning},
  author={Abhijit Guha Roy and Shayan Siddiqui and Sebastian P{\"o}lsterl and Nassir Navab and Christian Wachinger},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.06731}
}


@article{Liu2022AsynchronousDF,
  title={Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations},
  author={Qi Liu and Bo-Jun Yang and Zhaojian Wang and Dafeng Zhu and Xinyi Wang and Kai Ma and Xinping Guan},
  journal={IEEE Transactions on Network Science and Engineering},
  year={2022},
  volume={9},
  pages={1680-1696}
}

@article{Chen2019AsynchronousOF,
  title={Asynchronous Online Federated Learning for Edge Devices},
  author={Yujing Chen and Yue Ning and Huzefa Rangwala},
  journal={ArXiv},
  year={2019},
  volume={abs/1911.02134}
}


@article{58f2965c5c4847d8b5e02e9e4408799d,
title = "The future of digital health with federated learning",
abstract = "Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.",
author = "Nicola Rieke and Jonny Hancox and Wenqi Li and Fausto Milletar{\`i} and Roth, {Holger R.} and Shadi Albarqouni and Spyridon Bakas and Galtier, {Mathieu N.} and Landman, {Bennett A.} and Klaus Maier-Hein and S{\'e}bastien Ourselin and Micah Sheller and Summers, {Ronald M.} and Andrew Trask and Daguang Xu and Maximilian Baust and Cardoso, {M. Jorge}",
year = "2020",
month = dec,
day = "1",
doi = "10.1038/s41746-020-00323-1",
language = "English",
volume = "3",
journal = "npj Digital Medicine",
issn = "2398-6352",
publisher = "Nature Publishing Group",
number = "1",
}

@article{CHENG2022102313,
title = {ResGANet: Residual group attention network for medical image classification and segmentation},
journal = {Medical Image Analysis},
volume = {76},
pages = {102313},
year = {2022},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2021.102313},
url = {https://www.sciencedirect.com/science/article/pii/S1361841521003583},
author = {Junlong Cheng and Shengwei Tian and Long Yu and Chengrui Gao and Xiaojing Kang and Xiang Ma and Weidong Wu and Shijia Liu and Hongchun Lu},
keywords = {Deep learning, Medical image analysis, Residual group attention network, Image classification, Image segmentation},
abstract = {In recent years, deep learning technology has shown superior performance in different fields of medical image analysis. Some deep learning architectures have been proposed and used for computational pathology classification, segmentation, and detection tasks. Due to their simple, modular structure, most downstream applications still use ResNet and its variants as the backbone network. This paper proposes a modular group attention block that can capture feature dependencies in medical images in two independent dimensions: channel and space. By stacking these group attention blocks in ResNet-style, we obtain a new ResNet variant called ResGANet. The stacked ResGANet architecture has 1.51–3.47 times fewer parameters than the original ResNet and can be directly used for downstream medical image segmentation tasks. Many experiments show that the proposed ResGANet is superior to state-of-the-art backbone models in medical image classification tasks. Applying it to different segmentation networks can improve the baseline model in medical image segmentation tasks without changing the network architecture. We hope that this work provides a promising method for enhancing the feature representation of convolutional neural networks (CNNs) in the future.}
}

@article{KHAIRANDISH2022290,
title = {A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images},
journal = {IRBM},
volume = {43},
number = {4},
pages = {290-299},
year = {2022},
issn = {1959-0318},
doi = {https://doi.org/10.1016/j.irbm.2021.06.003},
url = {https://www.sciencedirect.com/science/article/pii/S1959031821000713},
author = {M.O. Khairandish and M. Sharma and V. Jain and J.M. Chatterjee and N.Z. Jhanjhi},
keywords = {MRI, Brain tumor analysis, ML, DL, CNN & SVM},
abstract = {Objective
In this research paper, the brain MRI images are going to classify by considering the excellence of CNN on a public dataset to classify Benign and Malignant tumors.
Materials and Methods
Deep learning (DL) methods due to good performance in the last few years have become more popular for Image classification. Convolution Neural Network (CNN), with several methods, can extract features without using handcrafted models, and eventually, show better accuracy of classification. The proposed hybrid model combined CNN and support vector machine (SVM) in terms of classification and with threshold-based segmentation in terms of detection.
Result
The findings of previous studies are based on different models with their accuracy as Rough Extreme Learning Machine (RELM)-94.233%, Deep CNN (DCNN)-95%, Deep Neural Network (DNN) and Discrete Wavelet Autoencoder (DWA)-96%, k-nearest neighbors (kNN)-96.6%, CNN-97.5%. The overall accuracy of the hybrid CNN-SVM is obtained as 98.4959%.
Conclusion
In today's world, brain cancer is one of the most dangerous diseases with the highest death rate, detection and classification of brain tumors due to abnormal growth of cells, shapes, orientation, and the location is a challengeable task in medical imaging. Magnetic resonance imaging (MRI) is a typical method of medical imaging for brain tumor analysis. Conventional machine learning (ML) techniques categorize brain cancer based on some handicraft property with the radiologist specialist choice. That can lead to failure in the execution and also decrease the effectiveness of an Algorithm. With a brief look came to know that the proposed hybrid model provides more effective and improvement techniques for classification.}
}

@article{PAN2022103824,
title = {Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma},
journal = {Biomedical Signal Processing and Control},
volume = {77},
pages = {103824},
year = {2022},
issn = {1746-8094},
doi = {https://doi.org/10.1016/j.bspc.2022.103824},
url = {https://www.sciencedirect.com/science/article/pii/S1746809422003469},
author = {Liangrui Pan and Hetian Wang and Lian Wang and Boya Ji and Mingting Liu and Mitchai Chongcheawchamnan and Jin Yuan and Shaoliang Peng},
keywords = {Osteosarcoma, Denoise, Transformer, Feature fusion, Framework, Diagnose},
abstract = {The degree of malignancy of osteosarcoma and its tendency to metastasize/spread mainly depend on the pathological grade (determined by observing the morphology of the tumor under a microscope). The purpose of this study is to use artificial intelligence to classify osteosarcoma histological images and to assess tumor survival and necrosis, which will help doctors reduce their workload, improve the accuracy of osteosarcoma cancer detection, and make a better prognosis for patients. The study proposes a typical transformer image classification framework by integrating noise reduction convolutional autoencoder and feature cross fusion learning (NRCA-FCFL) to classify osteosarcoma histological images. Noise reduction convolutional autoencoder could well denoise histological images of osteosarcoma, resulting in more pure images for osteosarcoma classification. Moreover, we introduce feature cross fusion learning, which integrates two scale image patches, to sufficiently explore their interactions by using additional classification tokens. As a result, a refined fusion feature is generated, which is fed to the residual neural network for label predictions. We conduct extensive experiments to evaluate the performance of the proposed approach. The experimental results demonstrate that our method outperforms the traditional and deep learning approaches on various evaluation metrics, with an accuracy of 99.17% to support osteosarcoma diagnosis.}
}


@article{doi:10.1080/02564602.2021.1937349,
author = {Sumit Tripathi and Taresh Sarvesh Sharan and Shiru Sharma and Neeraj Sharma},
title = {An Augmented Deep Learning Network with Noise Suppression Feature for Efficient Segmentation of Magnetic Resonance Images},
journal = {IETE Technical Review},
volume = {39},
number = {4},
pages = {960-973},
year  = {2022},
publisher = {Taylor & Francis},
doi = {10.1080/02564602.2021.1937349},
URL = { https://doi.org/10.1080/02564602.2021.1937349}
}


@ARTICLE{9139480,
  author={Noothout, Julia M. H. and De Vos, Bob D. and Wolterink, Jelmer M. and Postma, Elbrich M. and Smeets, Paul A. M. and Takx, Richard A. P. and Leiner, Tim and Viergever, Max A. and Išgum, Ivana},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images}, 
  year={2020},
  volume={39},
  number={12},
  pages={4011-4022},
  doi={10.1109/TMI.2020.3009002}}


@inproceedings{10.1007/978-3-030-60548-3_18,
author = {Roth, Holger R. and Chang, Ken and Singh, Praveer and Neumark, Nir and Li, Wenqi and Gupta, Vikash and Gupta, Sharut and Qu, Liangqiong and Ihsani, Alvin and Bizzo, Bernardo C. and Wen, Yuhong and Buch, Varun and Shah, Meesam and Kitamura, Felipe and Mendon\c{c}a, Matheus and Lavor, Vitor and Harouni, Ahmed and Compas, Colin and Tetreault, Jesse and Dogra, Prerna and Cheng, Yan and Erdal, Selnur and White, Richard and Hashemian, Behrooz and Schultz, Thomas and Zhang, Miao and McCarthy, Adam and Yun, B. Min and Sharaf, Elshaimaa and Hoebel, Katharina V. and Patel, Jay B. and Chen, Bryan and Ko, Sean and Leibovitz, Evan and Pisano, Etta D. and Coombs, Laura and Xu, Daguang and Dreyer, Keith J. and Dayan, Ittai and Naidu, Ram C. and Flores, Mona and Rubin, Daniel and Kalpathy-Cramer, Jayashree},
title = {Federated Learning for Breast Density Classification: A Real-World Implementation},
year = {2020},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
booktitle = {Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning: Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings},
pages = {181–191},
numpages = {11},
}

@article{97c9f5c0c4714251a5c377616bf32211,
title = "Federated learning and differential privacy for medical image analysis",
author = "Mohammed Adnan and Shivam Kalra and Cresswell, {Jesse C.} and Taylor, {Graham W.} and Tizhoosh, {Hamid R.}",
year = "2022",
month = dec,
doi = "10.1038/s41598-022-05539-7",
language = "English (US)",
volume = "12",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",
}

@article{Jiang_Wang_Dou_2022, title={HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images}, volume={36}, url={https://ojs.aaai.org/index.php/AAAI/article/view/19993}, DOI={10.1609/aaai.v36i1.19993}, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Jiang, Meirui and Wang, Zirui and Dou, Qi}, year={2022}, month={Jun.}, pages={1087-1095} }

@ARTICLE{9268161,
  author={Yan, Zengqiang and Wicaksana, Jeffry and Wang, Zhiwei and Yang, Xin and Cheng, Kwang-Ting},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={Variation-Aware Federated Learning With Multi-Source Decentralized Medical Image Data}, 
  year={2021},
  volume={25},
  number={7},
  pages={2615-2628},
  doi={10.1109/JBHI.2020.3040015}}


@InProceedings{GuangyaoERB,
author={Zheng, Guangyao and Zhou, Samson and Braverman, Vladimir and Jacobs, Michael A. and Parekh, Vishwa S.},
title={Selective experience replay compression using coresets for lifelong  deep reinforcement learning in medical imaging},
year={2023},
publisher={Medical Imaging with Deep Learning}
}

@InProceedings{10.1007/978-3-030-00928-1_54,
author="Karani, Neerav
and Chaitanya, Krishna
and Baumgartner, Christian
and Konukoglu, Ender",
editor="Frangi, Alejandro F.
and Schnabel, Julia A.
and Davatzikos, Christos
and Alberola-L{\'o}pez, Carlos
and Fichtinger, Gabor",
title="A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2018",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="476--484",
abstract="Convolutional neural networks (CNNs) have shown promising results on several segmentation tasks in magnetic resonance (MR) images. However, the accuracy of CNNs may degrade severely when segmenting images acquired with different scanners and/or protocols as compared to the training data, thus limiting their practical utility. We address this shortcoming in a lifelong multi-domain learning setting by treating images acquired with different scanners or protocols as samples from different, but related domains. Our solution is a single CNN with shared convolutional filters and domain-specific batch normalization layers, which can be tuned to new domains with only a few ({\$}{\$}{\{}{\backslash}approx {\}}{\$}{\$}4) labelled images. Importantly, this is achieved while retaining performance on the older domains whose training data may no longer be available. We evaluate the method for brain structure segmentation in MR images. Results demonstrate that the proposed method largely closes the gap to the benchmark, which is training a dedicated CNN for each scanner.",
isbn="978-3-030-00928-1"
}

@inproceedings{Yoon2020FederatedCL,
  title={Federated Continual Learning with Weighted Inter-client Transfer},
  author={Jaehong Yoon and Wonyoung Jeong and Giwoong Lee and Eunho Yang and Sung Ju Hwang},
  booktitle={International Conference on Machine Learning},
  year={2020}
}

@INPROCEEDINGS{8967908,
  author={Liu, Boyi and Wang, Lujia and Liu, Ming},
  booktitle={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
  title={Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems}, 
  year={2019},
  volume={},
  number={},
  pages={1688-1695},
  doi={10.1109/IROS40897.2019.8967908}}

@article{rolnick2019experience,
  title={Experience replay for continual learning},
  author={Rolnick, David and Ahuja, Arun and Schwarz, Jonathan and Lillicrap, Timothy and Wayne, Gregory},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  year={2019}
}
@article{zheng2023selective,
  title={Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging},
  author={Zheng, Guangyao and Zhou, Samson and Braverman, Vladimir and Jacobs, Michael A and Parekh, Vishwa S},
  journal={arXiv preprint arXiv:2302.11510},
  year={2023}
}

@inproceedings{10.1007/978-3-030-91387-8_1,
author = {Yoo, Joo Hun and Jeong, Hyejun and Lee, Jaehyeok and Chung, Tai-Myoung},
title = {Federated Learning: Issues in Medical Application},
year = {2021},
publisher = {Springer},
address = {Berlin, Heidelberg},
booktitle = {Future Data and Security Engineering: 8th International Conference, FDSE 2021, Virtual Event, November 24–26, 2021, Proceedings},
pages = {3–22},
numpages = {20},
}

@misc{2204.13591,
Author = {Yixing Huang and Christoph Bert and Stefan Fischer and Manuel Schmidt and Arnd Dörfler and Andreas Maier and Rainer Fietkau and Florian Putz},
Title = {Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification},
Year = {2022},
Eprint = {arXiv:2204.13591},
}

@inproceedings{karani2018lifelong,
  title={A lifelong learning approach to brain MR segmentation across scanners and protocols},
  author={Karani, Neerav and Chaitanya, Krishna and Baumgartner, Christian and Konukoglu, Ender},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={476--484},
  year={2018},
  organization={Springer}
}

@inproceedings{derakhshani2022lifelonger,
  title={Lifelonger: A benchmark for continual disease classification},
  author={Derakhshani, Mohammad Mahdi and Najdenkoska, Ivona and van Sonsbeek, Tom and Zhen, Xiantong and Mahapatra, Dwarikanath and Worring, Marcel and Snoek, Cees GM},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={314--324},
  year={2022},
  organization={Springer}
}

@inproceedings{isele2018selective,
  title={Selective experience replay for lifelong learning},
  author={Isele, David and Cosgun, Akansel},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={32},
  year={2018}
}

@article{kinahan2019data,
  title={Data from the acrin 6668 trial nsclc-fdg-pet},
  author={Kinahan, Paul and Muzi, Mark and Bialecki, Brian and Herman, Ben and Coombs, Laura},
  journal={Cancer Imaging Arch},
  volume={10},
  number={10.7937},
  year={2019}
}

@article{machtay2013prediction,
  title={Prediction of survival by [18F] fluorodeoxyglucose positron emission tomography in patients with locally advanced non--small-cell lung cancer undergoing definitive chemoradiation therapy: results of the ACRIN 6668/RTOG 0235 trial},
  author={Machtay, Mitchell and Duan, Fenghai and Siegel, Barry A and Snyder, Bradley S and Gorelick, Jeremy J and Reddin, Janet S and Munden, Reginald and Johnson, Douglas W and Wilf, Larry H and DeNittis, Albert and others},
  journal={Journal of clinical oncology},
  volume={31},
  number={30},
  pages={3823},
  year={2013},
  publisher={American Society of Clinical Oncology}
}

@article{clark2013cancer,
  title={The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository},
  author={Clark, Kenneth and Vendt, Bruce and Smith, Kirk and Freymann, John and Kirby, Justin and Koppel, Paul and Moore, Stephen and Phillips, Stanley and Maffitt, David and Pringle, Michael and others},
  journal={Journal of digital imaging},
  volume={26},
  pages={1045--1057},
  year={2013},
  publisher={Springer}
}

@article{bakr2017data,
  title={Data for NSCLC radiogenomics collection},
  author={Bakr, Shaimaa and Gevaert, Olivier and Echegaray, Sebastian and Ayers, Kelsey and Zhou, Mu and Shafiq, Majid and Zheng, Hong and Zhang, Weiruo and Leung, Ann and Kadoch, Michael and others},
  journal={The Cancer Imaging Archive},
  volume={10},
  pages={K9},
  year={2017}
}

@article{bakr2018radiogenomic,
  title={A radiogenomic dataset of non-small cell lung cancer},
  author={Bakr, Shaimaa and Gevaert, Olivier and Echegaray, Sebastian and Ayers, Kelsey and Zhou, Mu and Shafiq, Majid and Zheng, Hong and Benson, Jalen Anthony and Zhang, Weiruo and Leung, Ann NC and others},
  journal={Scientific data},
  volume={5},
  number={1},
  pages={1--9},
  year={2018},
  publisher={Nature Publishing Group}
}

@article{gevaert2012non,
  title={Non--small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data—methods and preliminary results},
  author={Gevaert, Olivier and Xu, Jiajing and Hoang, Chuong D and Leung, Ann N and Xu, Yue and Quon, Andrew and Rubin, Daniel L and Napel, Sandy and Plevritis, Sylvia K},
  journal={Radiology},
  volume={264},
  number={2},
  pages={387--396},
  year={2012},
  publisher={Radiological Society of North America, Inc.}
}

@article{antonelli2022medical,
  title={The medical segmentation decathlon},
  author={Antonelli, Michela and Reinke, Annika and Bakas, Spyridon and Farahani, Keyvan and Kopp-Schneider, Annette and Landman, Bennett A and Litjens, Geert and Menze, Bjoern and Ronneberger, Olaf and Summers, Ronald M and others},
  journal={Nature communications},
  volume={13},
  number={1},
  pages={4128},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

@article{leung2020longitudinal,
  title={Longitudinal functional and imaging outcome measures in FKRP limb-girdle muscular dystrophy},
  author={Leung, Doris G and Bocchieri, Alex E and Ahlawat, Shivani and Jacobs, Michael A and Parekh, Vishwa S and Braverman, Vladimir and Summerton, Katherine and Mansour, Jennifer and Bibat, Genila and Morris, Carl and others},
  journal={BMC Neurology},
  volume={20},
  pages={1--12},
  year={2020},
  publisher={BioMed Central}
}Longitudinal functional and imaging outcome measures in FKRP limb-girdle muscular dystrophy