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

Search research articles

Related Experiment Video

Updated: May 31, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Explainable hybrid CNN-transformer with self-supervised learning for structural analysis of paranasal sinus CT.

Najeeb Ullah¹, Shabbab Ali Algamdi², Tariq Sadad³

¹College of Computer and Systems Engineering, Abdullah Al Salem University, Khaldiya, Kuwait.

Frontiers in Computational Neuroscience

|May 29, 2026

Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Urban Parks Act as Refuges for Avian Biodiversity in Chaoyang District, Beijing (China).

Ecology and evolution·2026

Same author

Glycine as a potential neuroprotective adjuvant to reduce cisplatin-induced brain inflammation in mice.

BMC neuroscience·2026

Same author

Production of exopolysaccharides from Lactobacillus species and optimization of physiochemical parameters by response surface methodology.

Journal of microbiological methods·2026

Same author

Neuroprotective effects of glycine against cisplatin-induced cognitive decline in mice.

Annals of medicine and surgery (2012)·2026

Same author

Low-Cost Agro-Industrial Waste-Based Production of Lipase by Aspergillus flavus and Its Application in Brassica napus Seed Germination.

Journal of oleo science·2026

Same author

Crystal structure of mouse DXO in complex with the UDP-N-acetylglucosamine cap and molecular mechanism for the decapping reactions.

Nucleic acids research·2026

This study introduces an explainable AI framework for analyzing paranasal sinus CT scans, improving anatomical assessment for ear, nose, and throat conditions.

Area of Science:

Medical Imaging
Artificial Intelligence
Anatomy

Background:

Accurate paranasal sinus evaluation aids in diagnosing ear, nose, and throat (ENT) conditions.
Deep learning methods struggle with complex sinus structures due to limited data and interpretability.

Purpose of the Study:

To develop an explainable hybrid AI framework for precise structural analysis of paranasal sinus CT scans.
To enhance the diagnosis and treatment of ENT conditions through improved anatomical assessment.

Main Methods:

Implemented an explainable hybrid CNN-Transformer framework with a self-supervised 3D convolutional autoencoder.
Utilized the multi-institutional CT-SCOPE dataset for evaluating diverse paranasal sinus CT volumes.
Combined anatomical segmentation with residual-based structural representation learning for anomaly detection.

Keywords:

autoencoder residual learning disease detection explainable AI medical image analysis transformer-based segmentation

Related Experiment Videos

Last Updated: May 31, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Main Results:

Achieved high anatomical fidelity with Dice similarity coefficients >0.83 across all four paranasal sinus regions.
Integrated convolutional and Transformer-based modeling for detailed structural and contextual analysis.
Generated reconstruction residual maps highlighting structural deviations and confirmed clinical relevance via Grad-CAM.

Conclusions:

The developed framework offers a comprehensive approach to paranasal sinus CT image analysis.
Establishes a foundation for AI-assisted diagnosis and future pathology-aware clinical modeling.