AIMC Topic: Algorithms

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Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques.

PloS one
Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various ma...

Eight quick tips for biologically and medically informed machine learning.

PLoS computational biology
Machine learning has become a powerful tool for computational analysis in the biomedical sciences, with its effectiveness significantly enhanced by integrating domain-specific knowledge. This integration has give rise to informed machine learning, in...

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...

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...

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...

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...

Comparison of active learning algorithms in classifying head computed tomography reports using bidirectional encoder representations from transformers.

International journal of computer assisted radiology and surgery
PURPOSE: Systems equipped with natural language (NLP) processing can reduce missed radiological findings by physicians, but the annotation costs are burden in the development. This study aimed to compare the effects of active learning (AL) algorithms...

Deep Equilibrium Unfolding Learning for Noise Estimation and Removal in Optical Molecular Imaging.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe no...

PADS-Net: GAN-based radiomics using multi-task network of denoising and segmentation for ultrasonic diagnosis of Parkinson disease.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Parkinson disease (PD) is a prevalent neurodegenerative disorder, and its accurate diagnosis is crucial for timely intervention. We propose the PArkinson disease Denoising and Segmentation Network (PADS-Net), to simultaneously denoise and segment tra...

An efficient deep learning system for automatic detection of Acute Lymphoblastic Leukemia.

ISA transactions
Early and highly accurate detection of rapidly damaging deadly disease like Acute Lymphoblastic Leukemia (ALL) is essential for providing appropriate treatment to save valuable lives. Recent development in deep learning, particularly transfer learnin...