Shifted Windows Transformers for Medical Image Quality Assessment

TL;DR

This paper introduces the use of Swin Transformer for medical image quality assessment, demonstrating improved accuracy over CNNs in certain tasks and marking the first application of vision transformers in this domain.

Contribution

The study pioneers the application of Swin Transformer for medical image quality assessment, showing improved classification performance on specific medical imaging tasks.

Findings

Swin Transformer improves classification accuracy on Chest X-Ray images.

Achieves 87.1% accuracy on Object-CXR dataset.

Achieves 95.48% accuracy on LVOT dataset.

Abstract

To maintain a standard in a medical imaging study, images should have necessary image quality for potential diagnostic use. Although CNN-based approaches are used to assess the image quality, their performance can still be improved in terms of accuracy. In this work, we approach this problem by using Swin Transformer, which improves the poor-quality image classification performance that causes the degradation in medical image quality. We test our approach on Foreign Object Classification problem on Chest X-Rays (Object-CXR) and Left Ventricular Outflow Tract Classification problem on Cardiac MRI with a four-chamber view (LVOT). While we obtain a classification accuracy of 87.1% and 95.48% on the Object-CXR and LVOT datasets, our experimental results suggest that the use of Swin Transformer improves the Object-CXR classification performance while obtaining a comparable performance for the LVOT dataset. To the best of our knowledge, our study is the first vision transformer application for medical image quality assessment.