Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Distance Corrections

Distance Corrections

To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Modulation of Respiratory Diseases by the Endocannabinoid System: A Therapeutic Perspective.

Basic & clinical pharmacology & toxicology·2026

Same author

Comprehensive effects of functional agents on growth, nutrient accumulation, and rhizosphere bacterial communities in flue-cured tobacco (<i>Nicotiana tabacum</i> L.).

Frontiers in plant science·2026

Same author

Seasonal Dynamics of Skin Microbiota and Metabolites in Transhumant-Grazed Altay Sheep.

Microorganisms·2026

Same author

Zinc-iron layered double hydroxide enhances cadmium tolerance in tobacco via cell wall immobilization and regulation of phenylpropanoid and nitrogen metabolism.

Journal of environmental management·2026

Same author

Quercetin Regulates Autophagy to Inhibit PRRSV Replication Through the PI3K/Akt/mTOR Signaling Pathway.

Viruses·2025

Same author

Identification and functional analysis of circular RNAs during mitochondrial damage induced by infectious bovine rhinotracheitis virus infection in Madin-Darby bovine kidney cells.

BMC genomics·2025

Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 26, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Semisupervised Semantic Segmentation with Mutual Correction Learning.

Yifan Xiao¹, Jing Dong¹, Dongsheng Zhou^1,2

¹Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China.

Computational Intelligence and Neuroscience

|October 13, 2022

Summary

This summary is machine-generated.

This study introduces a semisupervised semantic segmentation method using mutual correction learning to improve pseudo-label accuracy. The approach enhances segmentation performance by correcting convergence directions and refining confidence maps.

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Related Experiment Videos

Last Updated: Aug 26, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Semisupervised semantic segmentation leverages unlabeled data to reduce labeling costs.
Pseudo-supervision performance is highly dependent on the quality of pseudo-labels.
Existing methods struggle with incorrect convergence directions in pseudo-supervision.

Purpose of the Study:

To propose a novel semisupervised semantic segmentation method.
To address the issue of inaccurate pseudo-labels in semisupervised learning.
To enhance the overall accuracy and reliability of semantic segmentation models.

Main Methods:

Developed a semisupervised semantic segmentation approach based on mutual correction learning.
Incorporated a multiscale feature fusion attention mechanism to generate calibrated segmentation confidence maps.
Introduced a mutual correction mechanism using consistency regularization for cross pseudo-supervision.

Main Results:

The proposed method demonstrated significant improvements in mean intersection over union (MIoU).
Achieved MIoU scores of 75.32%, 77.80%, 78.95%, and 79.16% with varying proportions of labeled data (1/16, 1/8, 1/4, 1/2) on PASCAL VOC 2012.
The experimental results indicate the method's advanced performance level.

Conclusions:

The mutual correction learning strategy effectively corrects the convergence direction of pseudo-supervision.
The multiscale feature fusion attention mechanism and mutual correction learning enhance segmentation accuracy.
The proposed method represents a significant advancement in semisupervised semantic segmentation.