AIMC Topic: Databases, Factual

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Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine.

Medical & biological engineering & computing
Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-based brain-computer interface (BCI). However, labeled EEG samples are generally scarce and expensive to collect, while unlabeled samples are considered to be abun...

α-Satellite: An AI-Driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States.

IEEE journal of biomedical and health informatics
The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond for community mitigation, by advancing capabilities of artificial intellige...

Feature fusion via Deep Random Forest for facial age estimation.

Neural networks : the official journal of the International Neural Network Society
In the last few years, human age estimation from face images attracted the attention of many researchers in computer vision and machine learning fields. This is due to its numerous applications. In this paper, we propose a new architecture for age es...

An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.

Computational and mathematical methods in medicine
Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In t...

Fast interactive medical image segmentation with weakly supervised deep learning method.

International journal of computer assisted radiology and surgery
PURPOSE: To achieve accurate image segmentation, which is the first critical step in medical image analysis and interventions, using deep neural networks seems a promising approach provided sufficiently large and diverse annotated data from experts. ...

Kidney segmentation from computed tomography images using deep neural network.

Computers in biology and medicine
BACKGROUND: The precise segmentation of kidneys and kidney tumors can help medical specialists to diagnose diseases and improve treatment planning, which is highly required in clinical practice. Manual segmentation of the kidneys is extremely time-co...

A deep learning approach based on convolutional LSTM for detecting diabetes.

Computational biology and chemistry
Diabetes is a chronic disease that occurs when the pancreas does not generate sufficient insulin or the body cannot effectively utilize the produced insulin. If it remains unidentified and untreated, then it could be very deadliest. One can lead a he...

Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database.

International journal of environmental research and public health
The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained undere...

Fully multi-target segmentation for breast ultrasound image based on fully convolutional network.

Medical & biological engineering & computing
Ultrasound image segmentation plays an important role in computer-aided diagnosis of breast cancer. Existing approaches focused on extracting the tumor tissue to characterize the tumor class. However, other tissues are also helpful for providing the ...