Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data

Silva, Santiago; Gutman, Boris A.; Romero, Eduardo; Thompson, Paul M.; Altmann, André; Lorenzi, Marco

doi:10.1109/isbi.2019.8759317

Cited by 144 publications

(72 citation statements)

References 17 publications

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“…Vepakomma et al [103] built several configurations upon a distributed deep learning method called SplitNN [36] to facilitate the health entities collaboratively training deep learning models without sharing sensitive raw data or model details. Silva et al [93] illustrated their federated learning framework by investigating brain structural relationships across diseases and clinical cohorts. Huang et al [45] sought to tackle the challenge of non-IID ICU patient data by clustering patients into clinically meaningful communities that captured similar diagnoses and geological locations and simultaneously training one model per community.…”

Section: Healthcarementioning

confidence: 99%

Federated Learning for Healthcare Informatics

Glicksberg

et al. 2020

J Healthc Inform Res

661

333

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Section: Healthcarementioning

confidence: 99%

Federated Learning for Healthcare Informatics

Glicksberg

et al. 2020

J Healthc Inform Res

661

333

View full text Add to dashboard Cite

“…Even though users are sovereign, which could be an advantage to avoid pernicious companies to access our data, if the users misbehave, there is no easy way to expel them (Song, 2018). Federated learning could potentially be a viable solution to guarantee the security of patient data (see "Validation and continuous improvement subsection"), as this de-centralized model-training paradigm allows to update a learning model without sharing individual information (Silva et al, 2018).…”

Section: Securitymentioning

confidence: 99%

Machine Learning for Clinical Decision-Making: Challenges and Opportunities

Sanchez-Martinez¹,

Cámara²,

Piella³

et al. 2019

Preprint

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The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making. The success of these tools is subjected to the understanding of the intrinsic processes being used during the classical pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous step to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with each of these tasks, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes.

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“…However, authoritative views suggest that fully anonymized clinical imaging data can be used for research purposes with appropriate safeguards (The Royal College of Radiologists, 2017), and such use of data may be further facilitated by federated analyses of data, where the research pipelines are brought to the data (rather than vice versa), both for structural and functional imaging (X. Li et al, 2020;Silva et al, 2019).…”

Section: Leveraging Enigma To Address Challenges In Ams-tbi Researchmentioning

confidence: 99%

Toward a Global and Reproducible Science for Brain Imaging in Neurotrauma: The ENIGMA Adult Moderate/Severe Traumatic Brain Injury Working Group

Olsen¹,

Babikian²,

Bigler³

et al. 2019

Preprint

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The global burden of mortality and morbidity caused by traumatic brain injury (TBI) is significant and the heterogeneity of TBI patients and the relatively small sample sizes of most current neuroimaging studies is a major challenge for scientific advances and clinical translation. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Adult moderate/severe TBI (AMS-TBI) working group aims to be a driving force for new discoveries in AMS-TBI by providing researchers world-wide with an effective framework and platform for large-scale cross-border collaboration and data sharing. Based on the principles of transparency, rigor, reproducibility and collaboration, we will facilitate the development and dissemination of multiscale and big data analysis pipelines for harmonized analyses in AMS-TBI using structural and functional neuroimaging in combination with nonimaging biomarkers, genetics, as well as clinical and behavioral measures. Ultimately, we will offer investigators an unprecedented opportunity to test important hypotheses about recovery and morbidity in AMS-TBI by taking advantage of our robust methods for largescale neuroimaging data analysis. In this consensus statement we outline the working group’s short-term, intermediate, and long-term goals.

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Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data

Cited by 144 publications

References 17 publications

Federated Learning for Healthcare Informatics

Federated Learning for Healthcare Informatics

Machine Learning for Clinical Decision-Making: Challenges and Opportunities

Toward a Global and Reproducible Science for Brain Imaging in Neurotrauma: The ENIGMA Adult Moderate/Severe Traumatic Brain Injury Working Group

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