Shifted Windows Transformers for Medical Image Quality Assessment

Autor:	Ozer, Caner, Guler, Arda, Cansever, Aysel Turkvatan, Alis, Deniz, Karaarslan, Ercan, Oksuz, Ilkay
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 10 pages, 3 figures, 4 tables. Accepted in 13th Machine Learning in Medical Imaging (MLMI 2022) workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.06034 Zobrazit plný text záznamu View this record from Arxiv