Current and emerging trends in medical image segmentation with deep learning
PH Conze, G Andrade-Miranda… - … on Radiation and …, 2023 - ieeexplore.ieee.org
In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …
learning has experienced a widespread interest in medical image analysis. Remarkably …
Federated learning for medical image analysis: A survey
Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …
the small sample size problem. Many recent studies suggest using multi-domain data …
Fedseg: Class-heterogeneous federated learning for semantic segmentation
Federated Learning (FL) is a distributed learning paradigm that collaboratively learns a
global model across multiple clients with data privacy-preserving. Although many FL …
global model across multiple clients with data privacy-preserving. Although many FL …
Federated learning for healthcare applications
Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …
become critical for healthcare tasks like in medical image analysis and human behavior …
[HTML][HTML] Self-supervised spatial–temporal transformer fusion based federated framework for 4D cardiovascular image segmentation
Availability of high-quality large annotated data is indeed a significant challenge in
healthcare. In addition, privacy concerns and data-sharing restrictions often hinder access to …
healthcare. In addition, privacy concerns and data-sharing restrictions often hinder access to …
Federated learning for medical imaging radiology
MH Rehman, W Hugo Lopez Pinaya… - The British Journal of …, 2023 - academic.oup.com
Federated learning (FL) is gaining wide acceptance across the medical AI domains. FL
promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability …
promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability …
Decentralized learning in healthcare: a review of emerging techniques
Recent developments in deep learning have contributed to numerous success stories in
healthcare. The performance of a deep learning model generally improves with the size of …
healthcare. The performance of a deep learning model generally improves with the size of …
Mixsegnet: Fusing multiple mixed-supervisory signals with multiple views of networks for mixed-supervised medical image segmentation
Z Wang, C Yang - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
Deep learning has driven remarkable advancements in medical image segmentation. The
requirement for comprehensive annotations, however, poses a significant challenge due to …
requirement for comprehensive annotations, however, poses a significant challenge due to …
Cuing without sharing: A federated cued speech recognition framework via mutual knowledge distillation
Cued Speech (CS) is a visual coding tool to encode spoken languages at the phonetic level,
which combines lip-reading and hand gestures to effectively assist communication among …
which combines lip-reading and hand gestures to effectively assist communication among …
Dynamic Data Sample Selection and Scheduling in Edge Federated Learning
Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It
enables distributed learning to train on cross-device data, achieving efficient performance …
enables distributed learning to train on cross-device data, achieving efficient performance …