[HTML][HTML] Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization
Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …
Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …
diagnoses and research which underpin many recent breakthroughs in medicine and …
[HTML][HTML] Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions
Removing the bias and variance of multicentre data has always been a challenge in large
scale digital healthcare studies, which requires the ability to integrate clinical features …
scale digital healthcare studies, which requires the ability to integrate clinical features …
Multicenter and multichannel pooling GCN for early AD diagnosis based on dual-modality fused brain network
For significant memory concern (SMC) and mild cognitive impairment (MCI), their
classification performance is limited by confounding features, diverse imaging protocols, and …
classification performance is limited by confounding features, diverse imaging protocols, and …
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …
imaging. However, these approaches primarily focus on supervised learning, assuming that …
Explainable, domain-adaptive, and federated artificial intelligence in medicine
Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in
each domain is driven by a growing body of annotated data, increased computational …
each domain is driven by a growing body of annotated data, increased computational …
Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting
Advances in artificial intelligence have cultivated a strong interest in developing and
validating the clinical utilities of computer-aided diagnostic models. Machine learning for …
validating the clinical utilities of computer-aided diagnostic models. Machine learning for …
Alzheimer's disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions
Alzheimer's Disease (AD) is the most prevalent and rapidly progressing neurodegenerative
disorder among the elderly and is a leading cause of dementia. AD results in significant …
disorder among the elderly and is a leading cause of dementia. AD results in significant …
Generative adversarial network constrained multiple loss autoencoder: A deep learning‐based individual atrophy detection for Alzheimer's disease and mild cognitive …
R Shi, C Sheng, S Jin, Q Zhang, S Zhang… - Human brain …, 2023 - Wiley Online Library
Exploring individual brain atrophy patterns is of great value in precision medicine for
Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the current …
Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the current …
[HTML][HTML] SAN-Net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization
There are considerable interests in automatic stroke lesion segmentation on magnetic
resonance (MR) images in the medical imaging field, as stroke is an important …
resonance (MR) images in the medical imaging field, as stroke is an important …