Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
[HTML][HTML] [18F] FDG-PET/CT radiomics and artificial intelligence in lung cancer: technical aspects and potential clinical applications
R Manafi-Farid, E Askari, I Shiri, C Pirich… - Seminars in nuclear …, 2022 - Elsevier
Lung cancer is the second most common cancer and the leading cause of cancer-related
death worldwide. Molecular imaging using [18 F] fluorodeoxyglucose Positron Emission …
death worldwide. Molecular imaging using [18 F] fluorodeoxyglucose Positron Emission …
Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning
I Shiri, A Vafaei Sadr, A Akhavan, Y Salimi… - European Journal of …, 2023 - Springer
Purpose Attenuation correction and scatter compensation (AC/SC) are two main steps
toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI …
toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI …
[HTML][HTML] Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study
Background Deep learning is a promising way to improve health care. Image-processing
medical disciplines, such as pathology, are expected to be transformed by deep learning …
medical disciplines, such as pathology, are expected to be transformed by deep learning …
Clinical application of AI-based PET images in oncological patients
J Dai, H Wang, Y Xu, X Chen, R Tian - Seminars in Cancer Biology, 2023 - Elsevier
Based on the advantages of revealing the functional status and molecular expression of
tumor cells, positron emission tomography (PET) imaging has been performed in numerous …
tumor cells, positron emission tomography (PET) imaging has been performed in numerous …
Myocardial perfusion SPECT imaging radiomic features and machine learning algorithms for cardiac contractile pattern recognition
A U-shaped contraction pattern was shown to be associated with a better Cardiac
resynchronization therapy (CRT) response. The main goal of this study is to automatically …
resynchronization therapy (CRT) response. The main goal of this study is to automatically …
Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study
This study aimed to investigate the diagnostic performance of machine learning-based
radiomics analysis to diagnose coronary artery disease status and risk from rest/stress …
radiomics analysis to diagnose coronary artery disease status and risk from rest/stress …
[HTML][HTML] Multi-institutional PET/CT image segmentation using federated deep transformer learning
Abstract Background and Objective Generalizable and trustworthy deep learning models for
PET/CT image segmentation necessitates large diverse multi-institutional datasets …
PET/CT image segmentation necessitates large diverse multi-institutional datasets …
Federated learning in ocular imaging: current progress and future direction
Advances in artificial intelligence deep learning (DL) have made tremendous impacts on the
field of ocular imaging over the last few years. Specifically, DL has been utilised to detect …
field of ocular imaging over the last few years. Specifically, DL has been utilised to detect …
Differential privacy preserved federated transfer learning for multi-institutional 68Ga-PET image artefact detection and disentanglement
Purpose Image artefacts continue to pose challenges in clinical molecular imaging, resulting
in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and …
in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and …