[HTML][HTML] Transformers in medical image analysis
Transformers have dominated the field of natural language processing and have recently
made an impact in the area of computer vision. In the field of medical image analysis …
made an impact in the area of computer vision. In the field of medical image analysis …
Multimodal learning with graphs
Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …
systems, ranging from dynamic networks in biology to interacting particle systems in physics …
Deep learning for Alzheimer's disease diagnosis: A survey
M Khojaste-Sarakhsi, SS Haghighi… - Artificial intelligence in …, 2022 - Elsevier
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a
progressive decline in cognitive abilities. Since AD starts several years before the onset of …
progressive decline in cognitive abilities. Since AD starts several years before the onset of …
[HTML][HTML] A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom
Multimodal medical data fusion has emerged as a transformative approach in smart
healthcare, enabling a comprehensive understanding of patient health and personalized …
healthcare, enabling a comprehensive understanding of patient health and personalized …
Multimodal graph learning based on 3D Haar semi-tight framelet for student engagement prediction
With the increasing availability of multimodal educational data, there is a growing need to
effectively integrate and exploit multiple data sources to enhance student engagement …
effectively integrate and exploit multiple data sources to enhance student engagement …
Integration of multi-omics data using adaptive graph learning and attention mechanism for patient classification and biomarker identification
With the rapid development and accumulation of high-throughput sequencing technology
and omics data, many studies have conducted a more comprehensive understanding of …
and omics data, many studies have conducted a more comprehensive understanding of …
[HTML][HTML] Medical transformer for multimodal survival prediction in intensive care: integration of imaging and non-imaging data
When clinicians assess the prognosis of patients in intensive care, they take imaging and
non-imaging data into account. In contrast, many traditional machine learning models rely …
non-imaging data into account. In contrast, many traditional machine learning models rely …
Multimodal brain age estimation using interpretable adaptive population-graph learning
Brain age estimation is clinically important as it can provide valuable information in the
context of neurodegenerative diseases such as Alzheimer's. Population graphs, which …
context of neurodegenerative diseases such as Alzheimer's. Population graphs, which …
Gradient modulated contrastive distillation of low-rank multi-modal knowledge for disease diagnosis
The fusion of multi-modal data, eg, medical images and genomic profiles, can provide
complementary information and further benefit disease diagnosis. However, multi-modal …
complementary information and further benefit disease diagnosis. However, multi-modal …
Self-paced semi-supervised feature selection with application to multi-modal Alzheimer's disease classification
Semi-supervised multi-modal learning has attracted much attention due to the expense and
scarcity of data labels, especially in disease diagnosis field. Most existing methods follow …
scarcity of data labels, especially in disease diagnosis field. Most existing methods follow …