AIMC Topic: Humans

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Graph neural networks for electroencephalogram analysis: Alzheimer's disease and epilepsy use cases.

Neural networks : the official journal of the International Neural Network Society
Electroencephalography (EEG) is widely used as a non-invasive technique for the diagnosis of several brain disorders, including Alzheimer's disease and epilepsy. Until recently, diseases have been identified over EEG readings by human experts, which ...

Wasserstein task embedding for measuring task similarities.

Neural networks : the official journal of the International Neural Network Society
Measuring similarities between different tasks is critical in a broad spectrum of machine learning problems, including transfer, multi-task, continual, and meta-learning. Most current approaches to measuring task similarities are architecture-depende...

Deep fuzzy physics-informed neural networks for forward and inverse PDE problems.

Neural networks : the official journal of the International Neural Network Society
As a grid-independent approach for solving partial differential equations (PDEs), Physics-Informed Neural Networks (PINNs) have garnered significant attention due to their unique capability to simultaneously learn from both data and the governing phy...

ChatDiff: A ChatGPT-based diffusion model for long-tailed classification.

Neural networks : the official journal of the International Neural Network Society
Long-tailed data distributions have been a major challenge for the practical application of deep learning. Information augmentation intends to expand the long-tailed data into uniform distribution, which provides a feasible way to mitigate the data s...

Artificial intelligence-based analysis of retinal fluid volume dynamics in neovascular age-related macular degeneration and association with vision and atrophy.

Eye (London, England)
BACKGROUND/OBJECTIVES: To characterise morphological changes in neovascular age-related macular degeneration (nAMD) during anti-angiogenic therapy and explore relationships with best-corrected visual acuity (BCVA) and development of macular atrophy (...

Predicting pedestrian-vehicle interaction severity at unsignalized intersections.

Traffic injury prevention
OBJECTIVES: This study aims to develop and validate a novel deep-learning model that predicts the severity of pedestrian-vehicle interactions at unsignalized intersections, distinctively integrating Transformer-based models with Multilayer Perceptron...

How the technologies behind self-driving cars, social networks, ChatGPT, and DALL-E2 are changing structural biology.

BioEssays : news and reviews in molecular, cellular and developmental biology
The performance of deep Neural Networks (NNs) in the text (ChatGPT) and image (DALL-E2) domains has attracted worldwide attention. Convolutional NNs (CNNs), Large Language Models (LLMs), Denoising Diffusion Probabilistic Models (DDPMs)/Noise Conditio...

Assessment of a fully-automated diagnostic AI software in prostate MRI: Clinical evaluation and histopathological correlation.

European journal of radiology
OBJECTIVE: This study aims to evaluate the diagnostic performance of a commercial, fully-automated, artificial intelligence (AI) driven software tool in identifying and grading prostate lesions in prostate MRI, using histopathological findings as the...

Artificial neural network-based prediction of multiple sclerosis using blood-based metabolomics data.

Multiple sclerosis and related disorders
Multiple sclerosis (MS) remains a challenging neurological condition for diagnosis and management and is often detected in late stages, delaying treatment. Artificial intelligence (AI) is emerging as a promising approach to extracting MS information ...

Distance guided generative adversarial network for explainable medical image classifications.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Despite the potential benefits of data augmentation for mitigating data insufficiency, traditional augmentation methods primarily rely on prior intra-domain knowledge. On the other hand, advanced generative adversarial networks (GANs) generate inter-...