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Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

Qi Dou, Tiffany Y. So, Meirui Jiang, Quande Liu, Varut Vardhanabhuti, Georgios Kaissis, Zeju Li, Weixin Si, Heather H. C. Lee, Kevin Yu, Zuxin Feng, Li Dong (+9 others)
2021 npj Digital Medicine  
amounts of sensitive data.  ...  AbstractData privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning  ...  Data sharing across the sites was not required, while the model benefitted from the generalizability enabled by multicenter learning with the inclusion of diverse data sources.  ... 
doi:10.1038/s41746-021-00431-6 pmid:33782526 pmcid:PMC8007806 fatcat:zaqvgiokk5cvhoxgiuybtrw5pi

Model-to-Data Approach for Deep Learning in Optical Coherence Tomography Intraretinal Fluid Segmentation

Nihaal Mehta, Cecilia S. Lee, Luísa S. M. Mendonça, Khadija Raza, Phillip X. Braun, Jay S. Duker, Nadia K. Waheed, Aaron Y. Lee
2020 JAMA ophthalmology  
A model-to-data approach to deep learning applied in ophthalmology avoided many of the traditional hurdles in large-scale deep learning, including data sharing, security, and privacy concerns.  ...  To determine whether a model-to-data deep learning approach (ie, validation of the algorithm without any data transfer) can be applied in ophthalmology.  ...  Moreover, although our deep learning model showed no statistically significant differences in performance vs human grading, its performance may have been improved with an even larger data set.  ... 
doi:10.1001/jamaophthalmol.2020.2769 pmid:32761143 pmcid:PMC7411940 fatcat:l3oogkqmzfdq3gnd423o5do2ye

Distributed learning: a reliable privacy-preserving strategy to change multicenter collaborations using AI

Margarita Kirienko, Martina Sollini, Gaia Ninatti, Daniele Loiacono, Edoardo Giacomello, Noemi Gozzi, Francesco Amigoni, Luca Mainardi, Pier Luca Lanzi, Arturo Chiti
2021 European Journal of Nuclear Medicine and Molecular Imaging  
We performed a literature search using the term "distributed learning" OR "federated learning" in the PubMed/MEDLINE and EMBASE databases.  ...  Sensitive data can get preserved since they are not shared for model development.  ...  assessing, monitoring, and improving delivered care in cancer patients [57] .  ... 
doi:10.1007/s00259-021-05339-7 pmid:33847779 pmcid:PMC8041944 fatcat:frw7koe2ivf2pbkoq7nz2wvwky

Federated Learning for Computational Pathology on Gigapixel Whole Slide Images [article]

Ming Y. Lu, Dehan Kong, Jana Lipkova, Richard J. Chen, Rajendra Singh, Drew F. K. Williamson, Tiffany Y. Chen, Faisal Mahmood
2020 arXiv   pre-print
Our results show that using federated learning, we can effectively develop accurate weakly supervised deep learning models from distributed data silos without direct data sharing and its associated complexities  ...  In this paper, we introduce privacy-preserving federated learning for gigapixel whole slide images in computational pathology using weakly-supervised attention multiple instance learning and differential  ...  Acknowledgements This work was supported in part by internal funds from BWH Pathology, NIH National Institute of General Medical Sciences (NIGMS) R35GM138216A (to F.M.), Google Cloud Research Grant and  ... 
arXiv:2009.10190v2 fatcat:vifdyjp54zfg7exhmqyt7bqs6m

A Multisite, Report-Based, Centralized Infrastructure for Feedback and Monitoring of Radiology AI/ML Development and Clinical Deployment [article]

Menashe Benjamin, Guy Engelhard, Alex Aisen, Yinon Aradi, Elad Benjamin
2020 arXiv   pre-print
, deployment, monitoring and continuous improvement of Artificial Intelligence (AI)/Machine Learning (ML) solutions in the real world.  ...  The method addresses proposed regulatory requirements for post-marketing surveillance and external data. Comprehensive multi-site data collection assists in reducing bias.  ...  performance improvements.  ... 
arXiv:2008.13781v1 fatcat:2wxg6xmy4vd67opxaw2rhcqa3q

Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence

Hugo Vrenken, Mark Jenkinson, Dzung Pham, Charles R G Guttmann, Deborah Pareto, Michel Paardekooper, Alexandra de Sitter, Maria A Rocca, Viktor Wottschel, M Jorge Cardoso, Frederik Barkhof, MAGNIMS Study Group
2021 Neurology  
Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging and the understanding  ...  Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques.  ...  Examples focused on federated deep learning, in which model parameters but not data are transferred between sites, are described by Chang et al. 54 and Remedios et al. 55 A limitation of such a federated  ... 
doi:10.1212/wnl.0000000000012884 pmid:34607924 pmcid:PMC8610621 fatcat:omzknqfwobexrde7unps3vwjcy

Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

Panagiotis Papadimitroulas, Lennart Brocki, Neo Christopher Chung, Wistan Marchadour, Franck Vermet, Laurent Gaubert, Vasilis Eleftheriadis, Dimitris Plachouris, Dimitris Visvikis, George C. Kagadis, Mathieu Hatt
2021 Physica medica (Testo stampato)  
Deep Neural Networks (DNN) have achieved outstanding performance and broad implementation in image processing tasks.  ...  The quantity of the available imaging data and the increased developments of Machine Learning (ML), particularly Deep Learning (DL), triggered the research on uncovering "hidden" biomarkers and quantitative  ...  Deep learning Conventional machine learning had limited success in translating radiomic features into improving classification and prediction of cancer in clinical settings.  ... 
doi:10.1016/j.ejmp.2021.03.009 pmid:33765601 fatcat:ejg4x3n4pjbwrdqw4vxukrk2cm

Artificial intelligence for predicting various conditions in spine surgery: a systematic review
Искусственный интеллект для прогнозирования различных состояний в вертебрологии: систематический обзор

V.S. Pereverzev, National Priorov Medical Research Center of Traumatology and Orthopedics, Moscow, Russian Federation, A.I. Kazmin, M.L. Sazhnev, A.A. Panteleev, S.V. Kolesov, National Priorov Medical Research Center of Traumatology and Orthopedics, Moscow, Russian Federation, National Priorov Medical Research Center of Traumatology and Orthopedics, Moscow, Russian Federation, National Priorov Medical Research Center of Traumatology and Orthopedics, Moscow, Russian Federation, National Priorov Medical Research Center of Traumatology and Orthopedics, Moscow, Russian Federation
2021 Гений oртопедии  
Results 20 publications were selected for systematic review, which presented data on the application of artificial intelligence, machine learning and neural networks to predict any condition in spine surgery  ...  Therefore, the application of AI in the clinical practice of the spine surgeons can improve treatment results.  ...  study demonstrates that generating of continuously learning making them excellent personalized and robust deep learning-based tools in complex clinical scenarios The frequency of non-standard indications  ... 
doi:10.18019/1028-4427-2021-27-6-813-820 fatcat:wsnko53npfb67pfsmumc23nfzi

MIRACUM: Medical Informatics in Research and Care in University Medicine

Till Acker, Johannes Bernarding, Harald Binder, Martin Boeker, Melanie Boerries, Philipp Daumke, Thomas Ganslandt, Jürgen Hesser, Gunther Höning, Michael Neumaier, Kurt Marquardt, Harald Renz (+9 others)
2018 Methods of Information in Medicine  
The methodological approach for shared data usage relies on a federated querying and analysis concept.  ...  Objectives: Sharing data shall be supported by common interoperable tools and services, in order to leverage the power of such data for biomedical discovery and moving towards a learning health system.  ...  Acknowledgment We are grateful to the many members of the MIRACUM team who have actively participated in the design and first implementation of the described architecture and data integration centers.  ... 
doi:10.3414/me17-02-0025 pmid:30016814 pmcid:PMC6178200 fatcat:5vgcr2psinbbdgkolpsdapqxee

The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning [article]

Raed Kontar, Naichen Shi, Xubo Yue, Seokhyun Chung, Eunshin Byon, Mosharaf Chowdhury, Judy Jin, Wissam Kontar, Neda Masoud, Maher Noueihed, Chinedum E. Okwudire, Garvesh Raskutti (+3 others)
2021 arXiv   pre-print
This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacy-preserving model training, coined as federated  ...  learning (FL).  ...  Federated learning im- [293] Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and proves site performance in multicenter deep learning Ameet Talwalkar.  ... 
arXiv:2111.05326v1 fatcat:bbgdhtuqcrhstgakt2vxuve2ca

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Xingping Zhang, Yanchun Zhang, Guijuan Zhang, Xingting Qiu, Wenjun Tan, Xiaoxia Yin, Liefa Liao
2022 Frontiers in Oncology  
In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation  ...  , and inadequate feature criteria, and multicenter validation.  ...  Federated learning (169) enables data conversion from multiple centers into mineable shared data while preserving privacy constraints.  ... 
doi:10.3389/fonc.2022.773840 pmid:35251962 pmcid:PMC8891653 fatcat:3h5tnm3aznb33k5ylkcd6tvs4e

Swarm Learning for decentralized and confidential clinical machine learning

Stefanie Warnat-Herresthal, COVID-19 Aachen Study (COVAS), Hartmut Schultze, Krishnaprasad Lingadahalli Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg, Ravi Sarveswara, Kristian Händler, Peter Pickkers, N. Ahmad Aziz, Sofia Ktena (+55 others)
2021 Nature  
Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge  ...  computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning.  ...  Fox Foundation and the Parkinson's Progression Markers Initiative (PPMI) for contributing RNA-seq data; the CORSAAR study group for additional blood transcriptome samples; the collaborators who contributed  ... 
doi:10.1038/s41586-021-03583-3 pmid:34040261 fatcat:5ule2vsgbngltmi6b7ubr24yga

Moonshot Acceleration Factor: Medical Imaging

Eva M. Sevick-Muraca, Richard A. Frank, Maryellen L. Giger, James L. Mulshine
2017 Cancer Research  
Steps to accelerate the translation and clinical adoption of cancer discoveries to meet the goals of the Cancer Moonshot include harnessing computational power and architectures, developing data sharing  ...  policies, and standardizing medical imaging and in vitro diagnostics.  ...  In tandem, steps to standardize data acquisition protocols and analysis methods should be implemented early on when developing new diagnostic technologies, including optical imaging, computer-aided pathology  ... 
doi:10.1158/0008-5472.can-17-1698 pmid:28993413 fatcat:pmyiei7mzbdy7p6kbels2kot4u

Vermont Oxford Network: a worldwide learning community

Erika M. Edwards, Danielle E. Y. Ehret, Roger F. Soll, Jeffrey D. Horbar
2019 Translational Pediatrics  
A health care learning community engages providers and families in a collaborative environment to improve outcomes.  ...  , is a worldwide learning community in newborn medicine.  ...  Participating hospitals are listed in http:// fp.amegroups.cn/cms/tp.2019.07.01-1.pdf.  ... 
doi:10.21037/tp.2019.07.01 pmid:31413952 pmcid:PMC6675680 fatcat:auhc5j23bbg7ln25vypdr5pk2i

D6.2 - Preliminary conclusions about Federated Learning applied to clinical data

Federico Álvarez, Santiago Zazo, Juan Parras, Alejandro Almodóvar, Patricia Alonso, Enrico Giampieri, Gastone Castellani, Lorenzo Sani, Cesare Rollo, Tiziana Sanavia, Anders Krogh, Íñigo Prada-Luengo (+3 others)
2021 Zenodo  
This report comprises the first contributions from different partners on Federated Learning (FL).  ...  This procedure shares the fundamental approach of FL, which consists of performing some local processing, sharing some intermediate information and updating the local information with some global innovation  ...  It means that a representation is judged on the performance in e.g. a classification or survival analysis task. We will develop the representation learning and compare to UMAP.  ... 
doi:10.5281/zenodo.5862590 fatcat:trd6rdi7pzcq7gcta62jcitwua
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