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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Multi-Label Retinal Disease Classification Using Transformers.

Manuel Alejandro Rodriguez, Hasan AlMarzouqi, Panos Liatsis

IEEE Journal of Biomedical and Health Informatics

|October 12, 2022

Summary

This summary is machine-generated.

This study introduces a new system for early detection of multiple retinal diseases using fundus images. The transformer-based model significantly improves diagnostic accuracy, aiding in blindness prevention.

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

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Published on: April 21, 2023

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Early detection of retinal diseases is crucial for preventing vision loss.
Current methods for diagnosing multiple retinal diseases simultaneously can be challenging.

Purpose of the Study:

To propose a novel multi-label classification system for detecting multiple retinal diseases from fundus images.
To introduce the MuReD dataset for multi-label retinal disease classification.
To evaluate the efficacy of a transformer-based model for this task.

Main Methods:

Construction of the MuReD dataset by combining publicly available fundus image datasets.
Application of post-processing steps for data quality assurance.
Utilizing a transformer-based model for image analysis and multi-label classification.

Main Results:

The proposed system achieved superior performance compared to state-of-the-art methods.
An improvement of 7.9% in AUC score for disease detection was observed.
An improvement of 8.1% in AUC score for disease classification was achieved.

Conclusions:

Transformer-based architectures show significant potential in medical imaging for retinal disease diagnosis.
The developed system offers a promising approach for early and accurate detection of multiple retinal diseases.
This research contributes to advancing automated diagnostic tools in ophthalmology.