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2021 | OriginalPaper | Chapter

MIINet: An Image Quality Improvement Framework for Supporting Medical Diagnosis

Authors : Quan Huu Cap, Hitoshi Iyatomi, Atsushi Fukuda

Published in: Pattern Recognition. ICPR International Workshops and Challenges

Publisher: Springer International Publishing

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Abstract

Medical images have been indispensable and useful tools for supporting medical experts in making diagnostic decisions. However, taken medical images especially throat and endoscopy images are normally hazy, lack of focus, or uneven illumination. Thus, these could difficult the diagnosis process for doctors. In this paper, we propose MIINet, a novel image-to-image translation network for improving quality of medical images by unsupervised translating low-quality images to the high-quality clean version. Our MIINet is not only capable of generating high-resolution clean images, but also preserving the attributes of original images, making the diagnostic more favorable for doctors. Experiments on dehazing 100 practical throat images show that our MIINet largely improves the mean doctor opinion score (MDOS), which assesses the quality and the reproducibility of the images from the baseline of 2.36 to 4.11, while dehazed images by CycleGAN got lower score of 3.83. The MIINet is confirmed by three physicians to be satisfying in supporting throat disease diagnostic from original low-quality images.

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Title: MIINet: An Image Quality Improvement Framework for Supporting Medical Diagnosis
Authors: Quan Huu Cap
Hitoshi Iyatomi
Atsushi Fukuda
Publisher: Springer International Publishing
Book: Pattern Recognition. ICPR International Workshops and Challenges
Print ISBN: 978-3-030-68762-5

Electronic ISBN: 978-3-030-68763-2

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-68763-2_19

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