Background To develop deep learning models for medical diagnosis, it is important to collect
more medical data from several medical institutions. Due to the regulations for privacy …

In deep learning, acquiring sufficient data is crucial for making informed decisions. However,
due to concerns regarding security and privacy, obtaining enough data for training models in …

Smart healthcare relies on artificial intelligence (AI) functions for learning and analysis of
patient data. Since large and diverse datasets for training of Machine Learning (ML) models …

Accepted: 12 January 2022 Deep Neural networks algorithms are recently used to solve
problems in medical imaging like no time ever. However, one of the main challenges for …

Background: Machine learning and artificial intelligence have shown promising results in
many areas and are driven by the increasing amount of available data. However, these data …

Healthcare is predominantly regarded as a crucial consideration in promoting the general
physical and mental health and well‐being of people around the world. The amount of data …

Medical institutions often revoke data access due to the privacy concern of patients.
Federated Learning (FL) is a collaborative learning paradigm that can generate an unbiased …

In the medical or healthcare industry, where, the already available information or data is
never sufficient, excellence can be performed with the help of Federated Learning (FL) by …

Data plays a vital role in deep learning model training. In large-scale medical image
analysis, data privacy and ownership make data gathering challenging in a centralized …

Lung nodule segmentation on computed tomography (CT) is at the same time one of the
most common and laborious tasks in oncological radiology. Fortunately, artificial intelligence …