AI Medical Compendium Journal:
IEEE journal of biomedical and health informatics

Showing 31 to 40 of 1081 articles

PEARL: Cascaded Self-Supervised Cross-Fusion Learning for Parallel MRI Acceleration.

IEEE journal of biomedical and health informatics
Supervised deep learning (SDL) methodology holds promise for accelerated magnetic resonance imaging (AMRI) but is hampered by the reliance on extensive training data. Some self-supervised frameworks, such as deep image prior (DIP), have emerged, elim...

Cognitive Load Prediction From Multimodal Physiological Signals Using Multiview Learning.

IEEE journal of biomedical and health informatics
Predicting cognitive load is a crucial issue in the emerging field of human-computer interaction and holds significant practical value, particularly in flight scenarios. Although previous studies have realized efficient cognitive load classification,...

Unsupervised Domain Adaptation for Cross-Modality Cerebrovascular Segmentation.

IEEE journal of biomedical and health informatics
Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) and computed tomography angiography (CTA) is essential in providing supportive information for diagnosing and treatment planning of multiple intracranial vascul...

ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation.

IEEE journal of biomedical and health informatics
Weakly-supervised segmentation (WSS) has emerged as a solution to mitigate the conflict between annotation cost and model performance by adopting sparse annotation formats (e.g., point, scribble, block, etc.). Typical approaches attempt to exploit an...

Multi-Scale Group Agent Attention-Based Graph Convolutional Decoding Networks for 2D Medical Image Segmentation.

IEEE journal of biomedical and health informatics
Automated medical image segmentation plays a crucial role in assisting doctors in diagnosing diseases. Feature decoding is a critical yet challenging issue for medical image segmentation. To address this issue, this work proposes a novel feature deco...

SFM-Net: Semantic Feature-Based Multi-Stage Network for Unsupervised Image Registration.

IEEE journal of biomedical and health informatics
It is difficult for general registration methods to establish the fine correspondence between images with complex anatomical structures. To overcome the above problem, this work presents SFM-Net, an unsupervised multi-stage semantic feature-based net...

DEMENTIA: A Hybrid Attention-Based Multimodal and Multi-Task Learning Framework With Expert Knowledge for Alzheimer's Disease Assessment From Speech.

IEEE journal of biomedical and health informatics
The prevalence of Alzheimer's disease (AD) is rising annually, imposing a severe burden on patients and society. Therefore, assisted AD assessment is crucial. The decline in language function and the cognitive impairment it reflects are key external ...

sEMG-Based Gesture Recognition via Multi-Feature Fusion Network.

IEEE journal of biomedical and health informatics
The sparse surface electromyography-based gesture recognition suffers from the problems of feature information not richness and poor generalization to small sample data. Therefore, a multi-feature fusion network (MFF-Net) model is proposed in this pa...

Multi-Scale Dynamic Sparse Token Multi-Instance Learning for Pathology Image Classification.

IEEE journal of biomedical and health informatics
In many challenging breast cancer pathology images, the proportion of truly informative tumor regions is extremely limited. The disparity between the essential information required for clinical diagnosis (Tumor area less than 10$\%$) and the vast amo...

Decoding SSVEP Via Calibration-Free TFA-Net: A Novel Network Using Time-Frequency Features.

IEEE journal of biomedical and health informatics
Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) signals offer high information transfer rates and non-invasive brain-to-device connectivity, making them highly practical. In recent years, deep learning technique...