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Found 10 papers, 0 papers with code
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.
Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations
We investigate the effect of image augmentations on reducing the requirement of labelled data in the semantic segmentation of chest X-rays for pneumonia detection.
Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans
We also present a comparison based on the segmentation results, the contextual information retained, and the inference time between this 3D technique and a traditional 2D deep learning technique.
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.
Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks
COVID-19 is an infectious disease that causes respiratory problems similar to those caused by SARS-CoV (2003).
Quantum Computing Methods for Supervised Learning
However, owing to its roots in quantum mechanics, research in this field has so far been confined within the purview of the physics community, and most work is not easily accessible to researchers from other disciplines.
Role of Edge Device and Cloud Machine Learning in Point-of-Care Solutions Using Imaging Diagnostics for Population Screening
Edge devices are revolutionizing diagnostics.
Automatic Grading of Knee Osteoarthritis on the Kellgren-Lawrence Scale from Radiographs Using Convolutional Neural Networks
Our method works in two connected stages: in the first stage, an object detection model segments individual knees from the rest of the image; in the second stage, a regression model automatically grades each knee separately on the KL scale.
Survey of Personalization Techniques for Federated Learning
Federated learning enables machine learning models to learn from private decentralized data without compromising privacy.
