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  • 1
    Online Resource
    Online Resource
    Federated learning enables big data for rare cancer boundary detection
    Springer Science and Business Media LLC ; 2022
    In:  Nature Communications Vol. 13, No. 1 ( 2022-12-05)
    Details
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2022-12-05)
    Abstract: Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature ( n  = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    URL: Article
    DOI: 10.1038/s41467-022-33407-5
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2553671-0
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  • 2
    Online Resource
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    NIMG-68. FEDERATED LEARNING IN NEURO-ONCOLOGY FOR MULTI-INSTITUTIONAL COLLABORATIONS WITHOUT SHARING PATIENT DATA
    Oxford University Press (OUP) ; 2019
    In:  Neuro-Oncology Vol. 21, No. Supplement_6 ( 2019-11-11), p. vi176-vi177
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 21, No. Supplement_6 ( 2019-11-11), p. vi176-vi177
    Abstract: Training deep learning algorithms requires large amounts of data, which is a significant challenge in the medical domain, and particularly in neuro-oncology, where ample data can only be found in multi-institutional collaborations. The current paradigm for multi-institutional collaborations is based on pooled datasets that has always faced privacy, legal, technical, and data-ownership concerns. In this study we evaluate the hypothesis that federated learning can provide a method to overcome these concerns and facilitate a shift in the paradigm of multi-institutional collaborations without sharing patient data. We attempt to investigate this hypothesis in a feasibility study of automatically delineating the glioblastoma extent in T2-FLAIR scans. METHODS We identified a retrospective cohort of 165 glioblastoma patients with available clinically acquired pre-operative multi-parametric structural MRI (mpMRI) scans (i.e., T1, T1Gd, T2, T2-FLAIR), with corresponding expert tumor boundary annotations, from 10 independent institutions. We implemented a 3D deep learning algorithm (3D-UNet) to predict the boundaries of the whole tumor extent, by virtue of the abnormal hyper-intense signal of T2-FLAIR scans. We compare the performance of this 3D-UNet model resulting from federated learning with the performance of the same 3D-UNet model generated by sharing data to a single location where centralized/traditional training occurs. RESULTS Our quantitative results on federated learning (Dice:85.2%) across individual contributions from the 10 institutions demonstrate final model quality reaching 99% of the model quality achieved by sharing data (Dice:86.2%). CONCLUSIONS Translation and adoption of federated learning in a clinical configuration for multi-institutional collaborations is expected to have a catalytic impact towards precision and personalized medicine. The performance of computer-aided analytics and assistive diagnostics is expected to see a precipitous rise, as new models are trained on datasets of unprecedented size through such data-private collaborations.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noz175.737
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 2094060-9
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  • 3
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    Online Resource
    OpenFL: the open federated learning library
    IOP Publishing ; 2022
    In:  Physics in Medicine & Biology Vol. 67, No. 21 ( 2022-11-07), p. 214001-
    Details
    In: Physics in Medicine & Biology, IOP Publishing, Vol. 67, No. 21 ( 2022-11-07), p. 214001-
    Abstract: Objective. Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) and deep learning (DL) projects without sharing sensitive data, such as patient records, financial data, or classified secrets. Approach. Open federated learning (OpenFL) framework is an open-source python-based tool for training ML/DL algorithms using the data-private collaborative learning paradigm of FL, irrespective of the use case. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and DL frameworks. Main results. In this manuscript, we present OpenFL and summarize its motivation and development characteristics, with the intention of facilitating its application to existing ML/DL model training in a production environment. We further provide recommendations to secure a federation using trusted execution environments to ensure explicit model security and integrity, as well as maintain data confidentiality. Finally, we describe the first real-world healthcare federations that use the OpenFL library, and highlight how it can be applied to other non-healthcare use cases. Significance. The OpenFL library is designed for real world scalability, trusted execution, and also prioritizes easy migration of centralized ML models into a federated training pipeline. Although OpenFL’s initial use case was in healthcare, it is applicable beyond this domain and is now reaching wider adoption both in research and production settings. The tool is open-sourced at github.com/intel/openfl .
    Type of Medium: Online Resource
    ISSN: 0031-9155 , 1361-6560
    URL: Article
    DOI: 10.1088/1361-6560/ac97d9
    RVK:
    WA 15000
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 1473501-5
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
    Springer Science and Business Media LLC ; 2020
    In:  Scientific Reports Vol. 10, No. 1 ( 2020-07-28)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2020-07-28)
    Abstract: Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and evaluate generalizability on data from institutions outside the federation. We further investigate the effects of data distribution across collaborating institutions on model quality and learning patterns, indicating that increased access to data through data private multi-institutional collaborations can benefit model quality more than the errors introduced by the collaborative method. Finally, we compare with other collaborative-learning approaches demonstrating the superiority of federated learning, and discuss practical implementation considerations. Clinical adoption of federated learning is expected to lead to models trained on datasets of unprecedented size, hence have a catalytic impact towards precision/personalized medicine.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-020-69250-1
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2615211-3
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  • 5
    Online Resource
    Online Resource
    NIMG-32. THE FEDERATED TUMOR SEGMENTATION (FETS) INITIATIVE: THE FIRST REAL-WORLD LARGE-SCALE DATA-PRIVATE COLLABORATION FOCUSING ON NEURO-ONCOLOGY
    Oxford University Press (OUP) ; 2021
    In:  Neuro-Oncology Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi135-vi136
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi135-vi136
    Abstract: Robustness and generalizability of artificial intelligent (AI) methods is reliant on the training data size and diversity, which are currently hindered in multi-institutional healthcare collaborations by data ownership and legal concerns. To address these, we introduce the Federated Tumor Segmentation (FeTS) Initiative, as an international consortium using federated learning (FL) for data-private multi-institutional collaborations, where AI models leverage data at participating institutions, without sharing data between them. The initial FeTS use-case focused on detecting brain tumor boundaries in MRI. METHODS The FeTS tool incorporates: 1) MRI pre-processing, including image registration and brain extraction; 2) automatic delineation of tumor sub-regions, by label fusion of pretrained top-performing BraTS methods; 3) tools for manual delineation refinements; 4) model training. 55 international institutions identified local retrospective cohorts of glioblastoma patients. Ground truth was generated using the first 3 FeTS functionality modes as mentioned earlier. Finally, the FL training mode comprises of i) an AI model trained on local data, ii) local model updates shared with an aggregator, which iii) combines updates from all collaborators to generate a consensus model, and iv) circulates the consensus model back to all collaborators for iterative performance improvements. RESULTS The first FeTS consensus model, from 23 institutions with data of 2,200 patients, showed an average improvement of 11.1% in the performance of the model on each collaborator’s validation data, when compared to a model trained on the publicly available BraTS data (n=231). CONCLUSION Our findings support that data increase alone would lead to AI performance improvements without any algorithmic development, hence indicating that the model performance would improve further when trained with all 55 collaborating institutions. FL enables AI model training with knowledge from data of geographically-distinct collaborators, without ever having to share any data, hence overcoming hurdles relating to legal, ownership, and technical concerns of data sharing.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noab196.532
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2094060-9
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  • 6
    Online Resource
    Online Resource
    The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research
    IOP Publishing ; 2022
    In:  Physics in Medicine & Biology Vol. 67, No. 20 ( 2022-10-21), p. 204002-
    Details
    In: Physics in Medicine & Biology, IOP Publishing, Vol. 67, No. 20 ( 2022-10-21), p. 204002-
    Abstract: Objective. De-centralized data analysis becomes an increasingly preferred option in the healthcare domain, as it alleviates the need for sharing primary patient data across collaborating institutions. This highlights the need for consistent harmonized data curation, pre-processing, and identification of regions of interest based on uniform criteria. Approach. Towards this end, this manuscript describes the Fe derated T umor S egmentation (FeTS) tool, in terms of software architecture and functionality. Main results. The primary aim of the FeTS tool is to facilitate this harmonized processing and the generation of gold standard reference labels for tumor sub-compartments on brain magnetic resonance imaging, and further enable federated training of a tumor sub-compartment delineation model across numerous sites distributed across the globe, without the need to share patient data. Significance. Building upon existing open-source tools such as the Insight Toolkit and Qt, the FeTS tool is designed to enable training deep learning models targeting tumor delineation in either centralized or federated settings. The target audience of the FeTS tool is primarily the computational researcher interested in developing federated learning models, and interested in joining a global federation towards this effort. The tool is open sourced at https://github.com/FETS-AI/Front-End .
    Type of Medium: Online Resource
    ISSN: 0031-9155 , 1361-6560
    URL: Article
    DOI: 10.1088/1361-6560/ac9449
    RVK:
    WA 15000
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 1473501-5
    SSG: 12
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  • 7
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    Author Correction: Federated learning enables big data for rare cancer boundary detection
    Springer Science and Business Media LLC ; 2023
    In:  Nature Communications Vol. 14, No. 1 ( 2023-01-26)
    Details
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 14, No. 1 ( 2023-01-26)
    Type of Medium: Online Resource
    ISSN: 2041-1723
    URL: Article
    DOI: 10.1038/s41467-023-36188-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2553671-0
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