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Data Element Mapping in the Data Privacy Era.

Studies in health technology and informatics
Secondary use of health data is made difficult in part because of large semantic heterogeneity. Many efforts are being made to align local terminologies with international standards. With increasing concerns about data privacy, we focused here on the...

Federated Learning in Healthcare: A Privacy Preserving Approach.

Studies in health technology and informatics
A need to enhance healthcare sector amidst pandemic arises. Many technological developments in Artificial Intelligence (AI) are being constantly leveraged in different fields of healthcare. One such advancement, Federated Learning(FL) has acquired re...

Applying Artificial Intelligence Privacy Technology in the Healthcare Domain.

Studies in health technology and informatics
Regulations set out strict restrictions on processing personal data. ML models must also adhere to these restrictions, as it may be possible to infer personal information from trained models. In this paper, we demonstrate the use of two novel AI Priv...

FedSPL: federated self-paced learning for privacy-preserving disease diagnosis.

Briefings in bioinformatics
The growing expansion of data availability in medical fields could help improve the performance of machine learning methods. However, with healthcare data, using multi-institutional datasets is challenging due to privacy and security concerns. Theref...

Barriers to artificial intelligence implementation in radiology practice: What the radiologist needs to know.

Radiologia
Artificial Intelligence has the potential to disrupt the way clinical radiology is practiced globally. However, there are barriers that radiologists should be aware of prior to implementing Artificial Intelligence in daily practice. Barriers include ...

Federated Learning via Conditional Mutual Learning for Alzheimer's Disease Classification on T1w MRI.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Data-driven deep learning has been considered a promising method for building powerful models for medical data, which often requires a large amount of diverse data to be sufficiently effective. However, the expensive cost of collecting and the privac...

Federated learning improves site performance in multicenter deep learning without data sharing.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL).

Distributed Skin Lesion Analysis Across Decentralised Data Sources.

Studies in health technology and informatics
Skin cancer has become the most common cancer type. Research has applied image processing and analysis tools to support and improve the diagnose process. Conventional procedures usually centralise data from various data sources to a single location a...

Federated Deep Learning Architecture for Personalized Healthcare.

Studies in health technology and informatics
Using deep learning to advance personalized healthcare requires data about patients to be collected and aggregated from disparate sources that often span institutions and geographies. Researchers regularly come face-to-face with legitimate security a...

Teledermatology in 2020: past, present and future perspectives.

Italian journal of dermatology and venereology
Born in 1995, teledermatology (TD) turns 25 years old today. Since then, TD evolved according to patients and physicians needs. The present review aimed to summarize all the efforts and experiences carried out in the field of TD and its subspecialtie...