Federated learning for healthcare domain-pipeline, applications and challenges
M Joshi, A Pal, M Sankarasubbu - ACM Transactions on Computing for …, 2022 - dl.acm.org
Federated learning is the process of developing machine learning models over datasets
distributed across data centers such as hospitals, clinical research labs, and mobile devices …
distributed across data centers such as hospitals, clinical research labs, and mobile devices …
Privacy challenges and research opportunities for genomic data sharing
L Bonomi, Y Huang, L Ohno-Machado - Nature genetics, 2020 - nature.com
The sharing of genomic data holds great promise in advancing precision medicine and
providing personalized treatments and other types of interventions. However, these …
providing personalized treatments and other types of interventions. However, these …
Secure genome-wide association analysis using multiparty computation
Most sequenced genomes are currently stored in strict access-controlled repositories,,. Free
access to these data could improve the power of genome-wide association studies (GWAS) …
access to these data could improve the power of genome-wide association studies (GWAS) …
Federated Random Forests can improve local performance of predictive models for various healthcare applications
Motivation Limited data access has hindered the field of precision medicine from exploring
its full potential, eg concerning machine learning and privacy and data protection rules. Our …
its full potential, eg concerning machine learning and privacy and data protection rules. Our …
[HTML][HTML] Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT
Abstract Machine learning applications for personalized medicine are highly dependent on
access to sufficient data. For personalized radiation oncology, datasets representing the …
access to sufficient data. For personalized radiation oncology, datasets representing the …
Princess: Privacy-protecting rare disease international network collaboration via encryption through software guard extensions
Motivation We introduce PRINCESS, a privacy-preserving international collaboration
framework for analyzing rare disease genetic data that are distributed across different …
framework for analyzing rare disease genetic data that are distributed across different …
The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition
Concerns regarding inappropriate leakage of sensitive personal information as well as
unauthorized data use are increasing with the growth of genomic data repositories …
unauthorized data use are increasing with the growth of genomic data repositories …
Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States
Accessing and integrating human genomic data with phenotypes are important for
biomedical research. Making genomic data accessible for research purposes, however …
biomedical research. Making genomic data accessible for research purposes, however …
[HTML][HTML] Privacy-preserving artificial intelligence techniques in biomedicine
R Torkzadehmahani, R Nasirigerdeh… - … of information in …, 2022 - thieme-connect.com
Background Artificial intelligence (AI) has been successfully applied in numerous scientific
domains. In biomedicine, AI has already shown tremendous potential, eg, in the …
domains. In biomedicine, AI has already shown tremendous potential, eg, in the …
Machine learning and genomics: precision medicine versus patient privacy
CA Azencott - … Transactions of the Royal Society A …, 2018 - royalsocietypublishing.org
Machine learning can have a major societal impact in computational biology applications. In
particular, it plays a central role in the development of precision medicine, whereby …
particular, it plays a central role in the development of precision medicine, whereby …