AIMC Topic: Algorithms

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Early detection of esophageal cancer: Evaluating AI algorithms with multi-institutional narrowband and white-light imaging data.

PloS one
Esophageal cancer is one of the most common cancers worldwide, especially esophageal squamous cell carcinoma, which is often diagnosed at a late stage and has a poor prognosis. This study aimed to develop an algorithm to detect tumors in esophageal e...

Domain generalization for image classification based on simplified self ensemble learning.

PloS one
Domain generalization seeks to acquire knowledge from limited source data and apply it to an unknown target domain. Current approaches primarily tackle this challenge by attempting to eliminate the differences between domains. However, as cross-domai...

Supervised Machine Learning and Physics Machine Learning approach for prediction of peak temperature distribution in Additive Friction Stir Deposition of Aluminium Alloy.

PloS one
Additive friction stir deposition (AFSD) is a novel solid-state additive manufacturing technique that circumvents issues of porosity, cracking, and properties anisotropy that plague traditional powder bed fusion and directed energy deposition approac...

Self-Supervised Graph Representation Learning for Single-Cell Classification.

Interdisciplinary sciences, computational life sciences
Accurately identifying cell types in single-cell RNA sequencing data is critical for understanding cellular differentiation and pathological mechanisms in downstream analysis. As traditional biological approaches are laborious and time-intensive, it ...

Localize-diffusion based dual-branch anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Due to the scarcity of real anomaly samples for use in anomaly detection studies, data augmentation methods are typically employed to generate pseudo anomaly samples to supplement the limited real samples. However, existing data augmentation methods ...

FSDM: An efficient video super-resolution method based on Frames-Shift Diffusion Model.

Neural networks : the official journal of the International Neural Network Society
Video super-resolution is a fundamental task aimed at enhancing video quality through intricate modeling techniques. Recent advancements in diffusion models have significantly enhanced image super-resolution processing capabilities. However, their in...

Machine learning approach for dosage individualization of azithromycin in children with community-acquired pneumonia.

British journal of clinical pharmacology
AIMS: The uncertainty about the efficacy and safety of currently used azithromycin dosing regimens in children warrants individualized therapy. The area under the plasma concentration-time curve over 24 h (AUC) of azithromycin correlates best with it...

Human Phenotype Ontology Annotations for Rare Congenital Conditions: Application to Arthrogryposis Multiplex Congenita.

American journal of medical genetics. Part A
Arthrogryposis multiplex congenita (AMC) represents a large, rare group of congenital conditions. This study addressed major challenges in AMC research posed by the lack of systematic frameworks for data collection and the use of inconsistent termino...

Learning-Based Models for Predicting IVIG Resistance and Coronary Artery Lesions in Kawasaki Disease: A Review of Technical Aspects and Study Features.

Paediatric drugs
Kawasaki disease (KD) is a common pediatric vasculitis, with coronary artery lesions (CALs) representing its most severe complication. Early identification of high-risk patients, including those with disease resistant to first-line treatments, is ess...

An extension to the OVH concept for knowledge-based dose volume histogram prediction in lung tumor volumetric-modulated arc therapy.

Journal of applied clinical medical physics
PURPOSE: Volumetric-modulated arc therapy (VMAT) treatment planning allows a compromise between a sufficient coverage of the planning target volume (PTV) and a simultaneous sparing of organs-at-risk (OARs). Particularly in the case of lung tumors, de...