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

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Optimizing deep learning models for glaucoma screening with vision transformers for resource efficiency and the pie

Sirikorn Sangchocanonta¹, Pakinee Pooprasert¹, Nichapa Lerthirunvibul¹

¹Center of Excellence in Nexus for Advanced Intelligence in Law, Engineering, and Medicine (Nail'Em), Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathum Thani, Thailand.

|March 21, 2025

Summary

This summary is machine-generated.

This study introduces a novel approach using Data-efficient image Transformers (DeiT) for faster glaucoma screening. Enhanced with the "pie method" and polar transformation, it achieves performance comparable to CNNs, improving early detection of vision impairment.

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Area of Science:

Ophthalmology and Artificial Intelligence
Medical Imaging Analysis
Computer Vision in Healthcare

Background:

Glaucoma is a leading cause of irreversible vision loss, necessitating early detection.
Current AI-based glaucoma screening primarily uses Convolutional Neural Networks (CNNs) on fundus images.
CNNs face challenges related to computational resources and processing time in large-scale screening.

Purpose of the Study:

To evaluate Data-efficient image Transformers (DeiT) for glaucoma screening, aiming to reduce computational demands.
To enhance DeiT performance through novel augmentation techniques for improved accuracy and efficiency.
To compare the efficacy of the proposed DeiT-based method against traditional CNN models.

Main Methods:

Utilized the GlauCUTU-DATA dataset, annotated by ophthalmologists with unanimous (3/3) and majority (2/3) agreement.
Implemented Data-efficient image Transformers (DeiT) with a preprocessing time reduction of 10x compared to CNNs.
Introduced the 'pie method' augmentation aligned with the ISNT rule and polar transformation to enhance cup visibility.

Main Results:

DeiT with enhancements achieved classification performance comparable to CNN models.
Sensitivity for glaucoma suspects increased by 40.18% (to 88.24%) using 3/3 data, excluding specific regions.
Area Under the Curve (AUC) values approached CNN performance, with the 3/3 dataset yielding AUCs around 92.63% ± 4.39% for glaucoma.

Conclusions:

The proposed DeiT-based approach offers a computationally efficient alternative for glaucoma screening.
The 'pie method' and polar transformation significantly improve DeiT's diagnostic accuracy.
DeiT's attention maps aid in localizing critical features like disc rim and notching, enhancing screening effectiveness.