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[HTML][HTML] Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review
Recent developments in the Internet of Things (IoT) and various communication
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
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 …
Autism spectrum disorder: neurodevelopmental risk factors, biological mechanism, and precision therapy
Autism spectrum disorder (ASD) is a heterogeneous, behaviorally defined
neurodevelopmental disorder. Over the past two decades, the prevalence of autism …
neurodevelopmental disorder. Over the past two decades, the prevalence of autism …
[HTML][HTML] Brain charts for the human lifespan
Over the past few decades, neuroimaging has become a ubiquitous tool in basic research
and clinical studies of the human brain. However, no reference standards currently exist to …
and clinical studies of the human brain. However, no reference standards currently exist to …
Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder
The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD)
are not well understood. Using a large neuroimaging dataset, we identified three latent …
are not well understood. Using a large neuroimaging dataset, we identified three latent …
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining
Despite advances in data augmentation and transfer learning, convolutional neural
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …
Domain adaptation for medical image analysis: a survey
Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …
from the domain shift problem caused by different distributions between source/reference …
[HTML][HTML] Multi-modality approaches for medical support systems: A systematic review of the last decade
Healthcare traditionally relies on single-modality approaches, which limit the information
available for medical decisions. However, advancements in technology and the availability …
available for medical decisions. However, advancements in technology and the availability …
Fedbn: Federated learning on non-iid features via local batch normalization
The emerging paradigm of federated learning (FL) strives to enable collaborative training of
deep models on the network edge without centrally aggregating raw data and hence …
deep models on the network edge without centrally aggregating raw data and hence …