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Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Model
Federated Learning (FL) helps AI models to generalize better and create a robust AI model by using data from different sources having different distributions and data characteristics without moving all the data to a central server.
Vulnerability Due to Training Order in Split Learning
Split learning (SL) is a privacy-preserving distributed deep learning method used to train a collaborative model without the need for sharing of patient's raw data between clients.
Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice
The use of machine learning to develop intelligent software tools for interpretation of radiology images has gained widespread attention in recent years.
Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly
We propose an automated method based on deep learning to compute the cardiothoracic ratio and detect the presence of cardiomegaly from chest radiographs.
Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare
In this paper, we compare three privacy-preserving distributed learning techniques: federated learning, split learning, and SplitFed.
