Related references
Note: Only part of the references are listed.A fast divide-and-conquer sparse Cox regression
Yan Wang et al.
BIOSTATISTICS (2021)
A novel privacy-preserving federated genome-wide association study framework and its application in identifying potential risk variants in ankylosing spondylitis
Xin Wu et al.
BRIEFINGS IN BIOINFORMATICS (2021)
Deep learning in Nuclear Medicine-focus on CNN-based approaches for PET/CT and PET/MR: where do we stand?
Margarita Kirienko et al.
CLINICAL AND TRANSLATIONAL IMAGING (2021)
Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement
Ji Eun Park et al.
EUROPEAN RADIOLOGY (2020)
The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system
Kenneth D. Mandl et al.
GENETICS IN MEDICINE (2020)
Distributed deep learning across multisite datasets for generalized CT hemorrhage segmentation
Samuel W. Remedios et al.
MEDICAL PHYSICS (2020)
Distributed learning on 20 000+lung cancer patients - The Personal Health Train
Timo M. Deist et al.
RADIOTHERAPY AND ONCOLOGY (2020)
A Survey on Distributed Machine Learning
Joost Verbraeken et al.
ACM COMPUTING SURVEYS (2020)
Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas
Margarita Kirienko et al.
RADIOLOGIA MEDICA (2020)
Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer
Marta Bogowicz et al.
SCIENTIFIC REPORTS (2020)
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta et al.
INFORMATION FUSION (2020)
Building machine learning models without sharing patient data: A simulation-based analysis of distributed learning by ensembling
Anup Tuladhar et al.
JOURNAL OF BIOMEDICAL INFORMATICS (2020)
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Micah J. Sheller et al.
SCIENTIFIC REPORTS (2020)
Multi-site fMRI analysis using privacy-preserving fe derate d learning and domain adaptation: ABIDE results
Xiaoxiao Li et al.
MEDICAL IMAGE ANALYSIS (2020)
Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology
Martina Sollini et al.
EUROPEAN JOURNAL OF HYBRID IMAGING (2020)
Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care
Fadila Zerka et al.
JCO CLINICAL CANCER INFORMATICS (2020)
The future of digital health with federated learning
Nicola Rieke et al.
NPJ DIGITAL MEDICINE (2020)
Accounting for data variability in multi-institutional distributed deep learning for medical imaging
Niranjan Balachandar et al.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2020)
Secure, privacy-preserving and federated machine learning in medical imaging
Georgios A. Kaissis et al.
NATURE MACHINE INTELLIGENCE (2020)
Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm
Rui Duan et al.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2020)
Federated electronic health records research technology to support clinical trial protocol optimization: Evidence from EHR4CR and the InSite platform
Brecht Claerhout et al.
JOURNAL OF BIOMEDICAL INFORMATICS (2019)
Distributed learning from multiple EHR databases: Contextual embedding models for medical events
Ziyi Li et al.
JOURNAL OF BIOMEDICAL INFORMATICS (2019)
Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics
Martina Sollini et al.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2019)
Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records
Li Huang et al.
JOURNAL OF BIOMEDICAL INFORMATICS (2019)
Manifold regularization based distributed semi-supervised learning algorithm using extreme learning machine over time-varying network
Jin Xie et al.
NEUROCOMPUTING (2019)
Digital twins to personalize medicine
Bergthor Bjornsson et al.
GENOME MEDICINE (2019)
Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory
Fida K. Dankar et al.
JMIR MEDICAL INFORMATICS (2019)
FEDERATED LEARNING IN DISTRIBUTED MEDICAL DATABASES: META-ANALYSIS OF LARGE-SCALE SUBCORTICAL BRAIN DATA
Santiago Silva et al.
2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019) (2019)
Federated learning of predictive models from federated Electronic Health Records
Theodora S. Brisimi et al.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS (2018)
Distributed learning of deep neural network over multiple agents
Otkrist Gupta et al.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS (2018)
Distributed deep learning networks among institutions for medical imaging
Ken Chang et al.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2018)
The 3M Approach to Cardiovascular Infections: Multimodality, Multitracers, and Multidisciplinary
Martina Sollini et al.
SEMINARS IN NUCLEAR MEDICINE (2018)
Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm
Koen Bruynseels et al.
FRONTIERS IN GENETICS (2018)
Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis
Junghye Lee et al.
JMIR MEDICAL INFORMATICS (2018)
Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries
Arthur Jochems et al.
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2017)
A framework for parallel and distributed training of neural networks
Simone Scardapane et al.
NEURAL NETWORKS (2017)
Multi-center machine learning in imaging psychiatry: A meta-model approach
Petr Dluhos et al.
NEUROIMAGE (2017)
Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT
Timo M. Deist et al.
CLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY (2017)
Building a Rapid Learning Health Care System for Oncology: Why CancerLinQ Collects Identifiable Health Information to Achieve Its Vision
Alaap Shah et al.
JOURNAL OF CLINICAL ONCOLOGY (2016)
Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept
Arthur Jochems et al.
RADIOTHERAPY AND ONCOLOGY (2016)
The National Cancer Data Base: A powerful initiative to improve cancer care in the United States
Karl Y. Bilimoria et al.
ANNALS OF SURGICAL ONCOLOGY (2008)
Share Paper
