S M Diakiw

No ORCID on file · 3 papers in corpus
other 2022
A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data
Scientific reports ·doi:10.1038/s41598-022-12833-x

Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centraliz…

other 2021
Automated detection of poor-quality data: case studies in healthcare
Scientific reports ·doi:10.1038/s41598-021-97341-0

The detection and removal of poor-quality data in a training set is crucial to achieve high-performing AI models. In healthcare, data can be inherently poor-quality due to uncertainty or subjectivity, but as is often the case, the requireme…

diagnostic-accuracy 2020
Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF
Human reproduction (Oxford, England) ·doi:10.1093/humrep/deaa013

STUDY QUESTION: Can an artificial intelligence (AI)-based model predict human embryo viability using images captured by optical light microscopy? SUMMARY ANSWER: We have combined computer vision image processing methods and deep learning t…