Deep learning for medical image-based cancer diagnosis
Simple Summary Deep learning has succeeded greatly in medical image-based cancer
diagnosis. To help readers better understand the current research status and ideas, this …
diagnosis. To help readers better understand the current research status and ideas, this …
Graph Artificial Intelligence in Medicine
In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks and graph transformer architectures, stands out for its capability to capture …
neural networks and graph transformer architectures, stands out for its capability to capture …
Graph ai in medicine
In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks (GNNs), stands out for its capability to capture intricate relationships within …
neural networks (GNNs), stands out for its capability to capture intricate relationships within …
CAMR: cross-aligned multimodal representation learning for cancer survival prediction
Motivation Accurately predicting cancer survival is crucial for helping clinicians to plan
appropriate treatments, which largely improves the life quality of cancer patients and spares …
appropriate treatments, which largely improves the life quality of cancer patients and spares …
Early prediction of high-cost inpatients with ischemic heart disease using network analytics and machine learning
P Yang, H Qiu, L Wang, L Zhou - Expert Systems with Applications, 2022 - Elsevier
Although identifying high-cost inpatients with ischemic heart disease (IHD) at the point of
admission is helpful for timely intervention and reducing costs, it is a difficult task due to the …
admission is helpful for timely intervention and reducing costs, it is a difficult task due to the …
Multimodality representation learning: A survey on evolution, pretraining and its applications
Multimodality Representation Learning, as a technique of learning to embed information
from different modalities and their correlations, has achieved remarkable success on a …
from different modalities and their correlations, has achieved remarkable success on a …
[HTML][HTML] Modelling-based joint embedding of histology and genomics using canonical correlation analysis for breast cancer survival prediction
Traditional approaches to predicting breast cancer patients' survival outcomes were based
on clinical subgroups, the PAM50 genes, or the histological tissue's evaluation. With the …
on clinical subgroups, the PAM50 genes, or the histological tissue's evaluation. With the …
Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions
In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural Networks
(GNNs), a domain where deep learning-based approaches have increasingly shown …
(GNNs), a domain where deep learning-based approaches have increasingly shown …
Graph neural networks in cancer and oncology research: Emerging and future trends
G Gogoshin, AS Rodin - Cancers, 2023 - mdpi.com
Simple Summary Graph Neural Networks are emerging as a powerful tool for structured data
analysis, and predictive modeling in massive multimodal datasets. In this review, we survey …
analysis, and predictive modeling in massive multimodal datasets. In this review, we survey …
DCCAFN: deep convolution cascade attention fusion network based on imaging genomics for prediction survival analysis of lung cancer
L Jia, X Ren, W Wu, J Zhao, Y Qiang… - Complex & Intelligent …, 2024 - Springer
Recently, lung cancer prediction based on imaging genomics has attracted great attention.
However, such studies often have many challenges, such as small sample size, high …
However, such studies often have many challenges, such as small sample size, high …