AIMC Topic: Humans

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A Brain-Inspired Framework for Evolutionary Artificial General Intelligence.

IEEE transactions on neural networks and learning systems
From the medical field to agriculture, from energy to transportation, every industry is going through a revolution by embracing artificial intelligence (AI); nevertheless, AI is still in its infancy. Inspired by the evolution of the human brain, this...

A Universal Approximation Result for Difference of Log-Sum-Exp Neural Networks.

IEEE transactions on neural networks and learning systems
We show that a neural network whose output is obtained as the difference of the outputs of two feedforward networks with exponential activation function in the hidden layer and logarithmic activation function in the output node, referred to as log-su...

Circular Complex-Valued GMDH-Type Neural Network for Real-Valued Classification Problems.

IEEE transactions on neural networks and learning systems
Recently, applications of complex-valued neural networks (CVNNs) to real-valued classification problems have attracted significant attention. However, most existing CVNNs are black-box models with poor explanation performance. This study extends the ...

Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons.

IEEE transactions on neural networks and learning systems
This article proposes an unsupervised address event representation (AER) object recognition approach. The proposed approach consists of a novel multiscale spatio-temporal feature (MuST) representation of input AER events and a spiking neural network ...

Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features.

Sensors (Basel, Switzerland)
This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classify the tumors. In particular, the deep features are extracted from a pre-trained convolutional neura...

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.

Nature communications
Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trai...

A machine learning-based clinical tool for diagnosing myopathy using multi-cohort microarray expression profiles.

Journal of translational medicine
BACKGROUND: Myopathies are a heterogenous collection of disorders characterized by dysfunction of skeletal muscle. In practice, myopathies are frequently encountered by physicians and precise diagnosis remains a challenge in primary care. Molecular e...

Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: For the growing patient population with congenital heart disease (CHD), improving clinical workflow, accuracy of diagnosis, and efficiency of analyses are considered unmet clinical needs. Cardiovascular magnetic resonance (CMR) imaging of...

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective.

BMC medical informatics and decision making
BACKGROUND: Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the l...

Machine learning approaches classify clinical malaria outcomes based on haematological parameters.

BMC medicine
BACKGROUND: Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malar...