[HTML][HTML] Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review

S Rani, A Kataria, S Kumar, P Tiwari - Knowledge-based systems, 2023‏ - Elsevier
Recent developments in the Internet of Things (IoT) and various communication
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

J Li, J Chen, Y Tang, C Wang, BA Landman… - Medical image …, 2023‏ - Elsevier
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

Federated learning for medical image analysis: A survey

H Guan, PT Yap, A Bozoki, M Liu - Pattern Recognition, 2024‏ - Elsevier
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 …

Autism spectrum disorder: neurodevelopmental risk factors, biological mechanism, and precision therapy

L Wang, B Wang, C Wu, J Wang, M Sun - International journal of …, 2023‏ - mdpi.com
Autism spectrum disorder (ASD) is a heterogeneous, behaviorally defined
neurodevelopmental disorder. Over the past two decades, the prevalence of autism …

[HTML][HTML] Brain charts for the human lifespan

RAI Bethlehem, J Seidlitz, SR White, JW Vogel… - Nature, 2022‏ - nature.com
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 …

Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder

AM Buch, PE Vértes, J Seidlitz, SH Kim… - Nature …, 2023‏ - nature.com
The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD)
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

B Billot, DN Greve, O Puonti, A Thielscher… - Medical image …, 2023‏ - Elsevier
Despite advances in data augmentation and transfer learning, convolutional neural
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …

Domain adaptation for medical image analysis: a survey

H Guan, M Liu - IEEE Transactions on Biomedical Engineering, 2021‏ - ieeexplore.ieee.org
Machine learning techniques used in computer-aided medical image analysis usually suffer
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

M Salvi, HW Loh, S Seoni, PD Barua, S García… - Information …, 2024‏ - Elsevier
Healthcare traditionally relies on single-modality approaches, which limit the information
available for medical decisions. However, advancements in technology and the availability …

Fedbn: Federated learning on non-iid features via local batch normalization

X Li, M Jiang, X Zhang, M Kamp, Q Dou - arxiv preprint arxiv:2102.07623, 2021‏ - arxiv.org
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 …