AIMC Topic: Databases, Factual

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A Deep Segmentation Network of Multi-Scale Feature Fusion Based on Attention Mechanism for IVOCT Lumen Contour.

IEEE/ACM transactions on computational biology and bioinformatics
Recently, coronary heart disease has attracted more and more attention, where segmentation and analysis for vascular lumen contour are helpful for treatment. And intravascular optical coherence tomography (IVOCT) images are used to display lumen shap...

Integrating Multi-Omic Data With Deep Subspace Fusion Clustering for Cancer Subtype Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
One type of cancer usually consists of several subtypes with distinct clinical implications, thus the cancer subtype prediction is an important task in disease diagnosis and therapy. Utilizing one type of data from molecular layers in biological syst...

An ECG Signal Classification Method Based on Dilated Causal Convolution.

Computational and mathematical methods in medicine
The incidence of cardiovascular disease is increasing year by year and is showing a younger trend. At the same time, existing medical resources are tight. The automatic detection of ECG signals becomes increasingly necessary. This paper proposes an a...

Comprehensive Review of Vision-Based Fall Detection Systems.

Sensors (Basel, Switzerland)
Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the m...

A Systematic Comparison of Depth Map Representations for Face Recognition.

Sensors (Basel, Switzerland)
Nowadays, we are witnessing the wide diffusion of active depth sensors. However, the generalization capabilities and performance of the deep face recognition approaches that are based on depth data are hindered by the different sensor technologies an...

Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets.

Chemical research in toxicology
Selecting a model in predictive toxicology often involves a trade-off between prediction performance and explainability: should we sacrifice the model performance to gain explainability or vice versa. Here we present a comprehensive study to assess a...

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.

Scientific reports
Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1)...

Deep Learning-Based Acute Ischemic Stroke Lesion Segmentation Method on Multimodal MR Images Using a Few Fully Labeled Subjects.

Computational and mathematical methods in medicine
Acute ischemic stroke (AIS) has been a common threat to human health and may lead to severe outcomes without proper and prompt treatment. To precisely diagnose AIS, it is of paramount importance to quantitatively evaluate the AIS lesions. By adopting...

Machine learning predictive model for severe COVID-19.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
To develop a modified predictive model for severe COVID-19 in people infected with Sars-Cov-2. We developed the predictive model for severe patients of COVID-19 based on the clinical date from the Tumor Center of Union Hospital affiliated with Tongji...

Machine Learning-Based Prediction of 6-Month Postoperative Karnofsky Performance Status in Patients with Glioblastoma: Capturing the Real-Life Interaction of Multiple Clinical and Oncologic Factors.

World neurosurgery
OBJECTIVE: Ability to thrive after invasive and intensive treatment is an important parameter to assess in patients with glioblastoma multiforme (GBM). Karnofsky Performance Status (KPS) is used to identify those patients suitable for postoperative r...