Dakka MA

No ORCID on file · 2 papers in corpus · active 2021-2022
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…

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…