AIMC Topic: Humans

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Characterization of hepatocellular carcinoma with CT with deep learning reconstruction compared with iterative reconstruction and 3-Tesla MRI.

European radiology
OBJECTIVES: This study compared the characteristics of lesions suspicious for hepatocellular carcinoma (HCC) and their LI-RADS classifications in adaptive statistical iterative reconstruction (ASIR) and deep learning reconstruction (DLR) to those of ...

Isles of autonomy: the rise of intelligent technologies.

Ergonomics
A critical metaphor for the development, implementation and penetration of autonomous machine systems into the world of human work is presented. Most especially, the ' concept is articulated which argues that the expropriation of human pre-eminence w...

Quantum mixed-state self-attention network.

Neural networks : the official journal of the International Neural Network Society
Attention mechanisms have revolutionized natural language processing. Combining them with quantum computing aims to further advance this technology. This paper introduces a novel Quantum Mixed-State Self-Attention Network (QMSAN) for natural language...

Dynamic planning in hierarchical active inference.

Neural networks : the official journal of the International Neural Network Society
By dynamic planning, we refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. A recent paradigm, active inference, brings fundamental insights into the adaptation of biological organisms, const...

Dual-view global and local category-attentive domain alignment for unsupervised conditional adversarial domain adaptation.

Neural networks : the official journal of the International Neural Network Society
Conditional adversarial domain adaptation (CADA) is one of the most commonly used unsupervised domain adaptation (UDA) methods. CADA introduces multimodal information to the adversarial learning process to align the distributions of the labeled sourc...

Mortality prediction after major surgery in a mixed population through machine learning: a multi-objective symbolic regression approach.

Anaesthesia
INTRODUCTION: Understanding 1-year mortality following major surgery offers valuable insights into patient outcomes and the quality of peri-operative care. Few models exist that predict 1-year mortality accurately. This study aimed to develop a predi...

Machine Learning of Endoscopy Images to Identify, Classify, and Segment Sinonasal Masses.

International forum of allergy & rhinology
BACKGROUND: We developed and assessed the performance of a machine learning model (MLM) to identify, classify, and segment sinonasal masses based on endoscopic appearance.

The Use of Artificial Intelligence for Endoscopic Evaluation of the Small Bowel.

Gastrointestinal endoscopy clinics of North America
There remains great potential for widespread implementation of artificial intelligence (AI) in managing small bowel disorders. Studies have shown excellent accuracy in diagnosing various diseases and lesions throughout the small bowel, with most show...

A Fine-grained Hemispheric Asymmetry Network for accurate and interpretable EEG-based emotion classification.

Neural networks : the official journal of the International Neural Network Society
In this work, we propose a Fine-grained Hemispheric Asymmetry Network (FG-HANet), an end-to-end deep learning model that leverages hemispheric asymmetry features within 2-Hz narrow frequency bands for accurate and interpretable emotion classification...

Improved analysis of supervised learning in the RKHS with random features: Beyond least squares.

Neural networks : the official journal of the International Neural Network Society
We consider kernel-based supervised learning using random Fourier features, focusing on its statistical error bounds and generalization properties with general loss functions. Beyond the least squares loss, existing results only demonstrate worst-cas...