AIMC Topic: Humans

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Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features.

International journal of environmental research and public health
This paper puts forward a new method of landscape recognition and evaluation by using aerial video and EEG technology. In this study, seven typical landscape types (forest, wetland, grassland, desert, water, farmland, and city) were selected. Differe...

Personal identification with artificial intelligence under COVID-19 crisis: a scoping review.

Systematic reviews
BACKGROUND: Artificial intelligence is useful for building objective and rapid personal identification systems. It is important to research and develop personal identification methods as social and institutional infrastructure. A critical considerati...

A pilot study of machine-learning based automated planning for primary brain tumours.

Radiation oncology (London, England)
PURPOSE: High-quality radiotherapy (RT) planning for children and young adults with primary brain tumours is essential to minimize the risk of late treatment effects. The feasibility of using automated machine-learning (ML) to aid RT planning in this...

DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning.

BMC bioinformatics
BACKGROUND: A limitation of traditional differential expression analysis on small datasets involves the possibility of false positives and false negatives due to sample variation. Considering the recent advances in deep learning (DL) based models, we...

Tracking cell lineages in 3D by incremental deep learning.

eLife
Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep-learning models. Moreover, annotation, training, prediction, and proofreading curre...

Accelerated cardiac T mapping in four heartbeats with inline MyoMapNet: a deep learning-based T estimation approach.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
PURPOSE: To develop and evaluate MyoMapNet, a rapid myocardial T mapping approach that uses fully connected neural networks (FCNN) to estimate T values from four T-weighted images collected after a single inversion pulse in four heartbeats (Look-Lock...

A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface.

Journal of neural engineering
Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor ima...

Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients.

PloS one
Mortality prediction for intensive care unit (ICU) patients is crucial for improving outcomes and efficient utilization of resources. Accessibility of electronic health records (EHR) has enabled data-driven predictive modeling using machine learning....

Attention based automated radiology report generation using CNN and LSTM.

PloS one
The automated generation of radiology reports provides X-rays and has tremendous potential to enhance the clinical diagnosis of diseases in patients. A new research direction is gaining increasing attention that involves the use of hybrid approaches ...

Predicting Visual Fields From Optical Coherence Tomography via an Ensemble of Deep Representation Learners.

American journal of ophthalmology
PURPOSE: To develop and validate a deep learning method of predicting visual function from spectral domain optical coherence tomography (SD-OCT)-derived retinal nerve fiber layer thickness (RNFLT) measurements and corresponding SD-OCT images.