Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40039412
Federated Learning (FL) is emerging in the medical field to address the need for diverse datasets while complying with data protection regulations. This decentralised learning paradigm allows hospitals (clients) to train machine learning models local...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40039001
This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked convolutional neural networks (CNNs). Our innovative technique leverages the distributed nature of fe...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40038952
Mental health conditions, prevalent across various demographics, necessitate efficient monitoring to mitigate their adverse impacts on life quality. The surge in data-driven methodologies for mental health monitoring has underscored the importance of...
Malignant glioma is the uncontrollable growth of cells in the spinal cord and brain that look similar to the normal glial cells. The most essential part of the nervous system is glial cells, which support the brain's functioning prominently. However,...
Computer methods and programs in biomedicine
39874935
BACKGROUND AND OBJECTIVE: Cloud-based Deep Learning as a Service (DLaaS) has transformed biomedicine by enabling healthcare systems to harness the power of deep learning for biomedical data analysis. However, privacy concerns emerge when sensitive us...
BACKGROUND: Considering the disruptive potential of AI technology, its current and future impact in healthcare, as well as healthcare professionals' lack of training in how to use it, the paper summarizes how to approach the challenges of AI from an ...
The proliferation of wearable sensors and mobile devices has fueled advancements in human activity recognition (HAR), with growing importance placed on both accuracy and privacy preservation. In this paper, the author proposes a federated learning fr...
The development and implementation of Artificial Intelligence (AI) health systems represent a great power that comes with great responsibility. Their capacity to improve and transform healthcare involves inevitable risks. A major risk in this regard ...
The traditional molecular-based identification (TMID) technique of new infections from genome sequences (GSs) has made significant contributions so far. However, due to the sensitive nature of the medical data, the TMID technique of transferring the ...
Integrating low-rank adaptation (LoRA) with federated learning (FL) has received widespread attention recently, aiming to adapt pretrained foundation models (FMs) to downstream medical tasks via privacy-preserving decentralized training. However, owi...