[HTML][HTML] Deep learning for chest X-ray analysis: A survey
E Çallı, E Sogancioglu, B van Ginneken… - Medical Image …, 2021 - Elsevier
Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …
image analysis tasks. As the most commonly performed radiological exam, chest …
How molecular imaging will enable robotic precision surgery: the role of artificial intelligence, augmented reality, and navigation
Molecular imaging is one of the pillars of precision surgery. Its applications range from early
diagnostics to therapy planning, execution, and the accurate assessment of outcomes. In …
diagnostics to therapy planning, execution, and the accurate assessment of outcomes. In …
Causality-driven graph neural network for early diagnosis of pancreatic cancer in non-contrast computerized tomography
X Li, R Guo, J Lu, T Chen, X Qian - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Pancreatic cancer is the emperor of all cancer maladies, mainly because there are no
characteristic symptoms in the early stages, resulting in the absence of effective screening …
characteristic symptoms in the early stages, resulting in the absence of effective screening …
Latent-graph learning for disease prediction
Abstract Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful
machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction. A key …
machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction. A key …
Graph convolutional networks for multi-modality medical imaging: Methods, architectures, and clinical applications
Image-based characterization and disease understanding involve integrative analysis of
morphological, spatial, and topological information across biological scales. The …
morphological, spatial, and topological information across biological scales. The …
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications
Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data
ubiquitous in the healthcare domain. Two prominent examples are molecule property …
ubiquitous in the healthcare domain. Two prominent examples are molecule property …
Multimodal graph attention network for COVID-19 outcome prediction
When dealing with a newly emerging disease such as COVID-19, the impact of patient-and
disease-specific factors (eg, body weight or known co-morbidities) on the immediate course …
disease-specific factors (eg, body weight or known co-morbidities) on the immediate course …
Fusion high-resolution network for diagnosing ChestX-ray images
Z Huang, J Lin, L Xu, H Wang, T Bai, Y Pang, TH Meen - Electronics, 2020 - mdpi.com
The application of deep convolutional neural networks (CNN) in the field of medical image
processing has attracted extensive attention and demonstrated remarkable progress. An …
processing has attracted extensive attention and demonstrated remarkable progress. An …
A CAD System for Lung Cancer Detection Using Chest X-ray: A Review
For many years, lung cancer has been ranked among the deadliest illnesses in the world.
Therefore, it must be anticipated and detected at an early stage. We need to build a …
Therefore, it must be anticipated and detected at an early stage. We need to build a …
Decision support for intoxication prediction using graph convolutional networks
Every day, poison control centers (PCC) are called for immediate classification and
treatment recommendations of acute intoxication cases. Due to their time-sensitive nature, a …
treatment recommendations of acute intoxication cases. Due to their time-sensitive nature, a …