Showing papers by "Vladimir Brusic published in 2023"

A privacy preserving framework for federated learning in smart healthcare systems

Abstract: Federated Learning (FL) is a platform for smart healthcare systems that use wearables and other Internet of Things enabled devices. However, source inference attacks (SIAs) can infer the connection between physiological data in training datasets with FL clients and reveal the identities of participants to the attackers. We propose a comprehensive smart healthcare framework for sharing physiological data, named FRESH, that is based on FL and ring signature defense from the attacks. In FRESH, physiological data are collected from individuals by wearable devices. These data are processed by edge computing devices (e.g., mobile phones, tablet PCs) that train ML models using local data. The model parameters are uploaded by edge computing devices to the central server for joint training of FL models of disease prediction. In this procedure, certificateless ring signature is used to hide the source of parameter updates during joint training for FL to effectively resist SIAs. In the proposed ring signature schema, an improved batch verification algorithm is designed to leverage additivity of linear operations on elliptic curves and to help reduce the computing workload of the server. Experimental results demonstrate that FRESH effectively reduces the success rate of SIAs and the batch verification method significantly improves the efficiency of signature verification. FRESH can be applied to large scale smart healthcare systems with FL involving large numbers of users.

Design, construction and performance testing of a 1.5 T cryogen-free low-temperature superconductor whole-body MRI magnet

Abstract: This work describes the design, construction and testing of a 1.5 T cryogen-free low-temperature superconductor niobium–titanium whole-body magnet with a bore diameter of 850 mm that is suitable for clinical magnetic resonance imaging (MRI) applications. The magnet is actively shielded and passively shimmed to achieve confinement within a 0.5 mT stray magnetic field at 2.5/4 m radial/axial positions and 12.1 ppm field inhomogeneity over a 45 mm diameter of spherical volume. The magnet is conductively refrigerated using a two-stage Gifford–McMahon cryocooler and can be maintained at a steady temperature below 5.7 K during MRI scanning. The MRI scanner assembled with the designed cryogen-free magnet demonstrated stable performance and provided MRI images with good quality and high contrast.

Protocol for Classification Single-Cell PBMC Types from Pathological Samples Using Supervised Machine Learning.

Abstract: Peripheral blood mononuclear cells (PBMC) are mixed subpopulations of blood cells composed of five cell types. PBMC are widely used in the study of the immune system, infectious diseases, cancer, and vaccine development. Single-cell transcriptomics (SCT) allows the labeling of cell types by gene expression patterns from biological samples. Classifying cells into cell types and states is essential for single-cell analyses, especially in the classification of diseases and the assessment of therapeutic interventions, and for many secondary analyses. Most of the classification of cell types from SCT data use unsupervised clustering or a combination of unsupervised and supervised methods including manual correction. In this chapter, we describe a protocol that uses supervised machine learning (ML) methods with SCT data for the classification of PBMC cell types in samples representing pathological states. This protocol has three parts: (1) data preprocessing, (2) labeling of reference PBMC SCT datasets and training supervised ML models, and (3) labeling new PBMC datasets from disease samples. This protocol enables building classification models that are of high accuracy and efficiency. Our example focuses on 10× Genomics technology but applies to datasets from other SCT platforms.