A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
2021
npj Digital Medicine
Preserved Fulltext
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
2020
JAMA ophthalmology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2021
European Journal of Nuclear Medicine and Molecular Imaging
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
, 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
2021
Neurology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2021
Physica medica (Testo stampato)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2021
Гений oртопедии
Искусственный интеллект для прогнозирования различных состояний в вертебрологии: систематический обзор
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2018
Methods of Information in Medicine
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2022
Frontiers in Oncology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2021
Nature
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2017
Cancer Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2019
Translational Pediatrics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2021
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 1,399 results