The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach

Federated Random Forests can improve local performance of predictive models for various healthcare applications

(2022) Anne-Christin Hauschild et al.   BIOINFORMATICS

The AIMe registry for artificial intelligence in biomedical research

(2021) Julian Matschinske et al.   NATURE METHODS

Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data

(2020) Micah J. Sheller et al.   Scientific Reports

Federated Learning on Clinical Benchmark Data: Performance Assessment

(2020) Geun Hyeong Lee et al.   JOURNAL OF MEDICAL INTERNET RESEARCH

Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist

(2020) Beau Norgeot et al.   NATURE MEDICINE

Advancing Drug Discovery via Artificial Intelligence

(2019) H.C. Stephen Chan et al.   TRENDS IN PHARMACOLOGICAL SCIENCES

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

(2018) Jianfang Liu et al.   CELL

A Survey on Homomorphic Encryption Schemes

(2018) Abbas Acar et al.   ACM COMPUTING SURVEYS

Artificial intelligence in healthcare

(2018) Kun-Hsing Yu et al.   Nature Biomedical Engineering

Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks

(2017) Andre Esteva et al.   NATURE

Singularity: Scientific containers for mobility of compute

(2017) Gregory M. Kurtzer et al.   PLoS One

Applications of Deep Learning in Biomedicine

(2016) Polina Mamoshina et al.   MOLECULAR PHARMACEUTICS