AIMC Topic: Supervised Machine Learning

Clear Filters Showing 1561 to 1570 of 1729 articles

Deep Learning-Enabled Detection of Pneumoperitoneum in Supine and Erect Abdominal Radiography: Modeling Using Transfer Learning and Semi-Supervised Learning.

Korean journal of radiology
OBJECTIVE: Detection of pneumoperitoneum using abdominal radiography, particularly in the supine position, is often challenging. This study aimed to develop and externally validate a deep learning model for the detection of pneumoperitoneum using sup...

COVID-19 disease identification network based on weakly supervised feature selection.

Mathematical biosciences and engineering : MBE
The coronavirus disease 2019 (COVID-19) outbreak has resulted in countless infections and deaths worldwide, posing increasing challenges for the health care system. The use of artificial intelligence to assist in diagnosis not only had a high accurac...

A Transferable Machine Learning Framework for Predicting Transcriptional Responses of Genes Across Species.

Methods in molecular biology (Clifton, N.J.)
Leveraging existing resources in studied species to predict gene functions has the potential to rapidly expand understanding of annotated genes in other, less well-studied, species with assembled genomes. However, orthology is not a reliable predicto...

Applying Machine Learning to Classify the Origins of Gene Duplications.

Methods in molecular biology (Clifton, N.J.)
Nearly all lineages of land plants have experienced at least one whole-genome duplication (WGD) in their history. The legacy of these ancient WGDs is still observable in the diploidized genomes of extant plants. Genes originating from WGD-paleologs-c...

An end-to-end multi-task system of automatic lesion detection and anatomical localization in whole-body bone scintigraphy by deep learning.

Bioinformatics (Oxford, England)
SUMMARY: Limited by spatial resolution and visual contrast, bone scintigraphy interpretation is susceptible to subjective factors, which considerably affects the accuracy and repeatability of lesion detection and anatomical localization. In this work...

FCCCSR_Glu: a semi-supervised learning model based on FCCCSR algorithm for prediction of glutarylation sites.

Briefings in bioinformatics
Glutarylation is a post-translational modification which plays an irreplaceable role in various functions of the cell. Therefore, it is very important to accurately identify the glutarylation substrates and its corresponding glutarylation sites. In r...

Important feature identification for perceptual sex of point-light walkers using supervised machine learning.

Journal of vision
The present study aimed to elucidate the dynamic features that are highly predictive in the biological and perceptual sex classification of point-light walkers (PLWs) and how these features behave in sex classification using supervised machine learni...

Semi-supervised Long-tail Endoscopic Image Classification.

Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih
Objective To explore the semi-supervised learning (SSL) algorithm for long-tail endoscopic image classification with limited annotations. Method We explored semi-supervised long-tail endoscopic image classification in HyperKvasir, the largest gastroi...

KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis.

Briefings in bioinformatics
Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describe them is principal component analysis (PCA) or the supervised linear discriminant analysis of princ...

Evaluation Tool to Diagnose Faults and Discrepancy in Semi-Automated or Manual Annotations in Ultrasound Cine Loops (Videos).

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Good quality (annotated) data is one of the most important aspects of supervised deep learning. Tasks such as semantic segmentation have a huge data requirement in exchange for only satisfactory performance. Large-scale annotations spread across mult...