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MIINet: An Image Quality Improvement Framework for Supporting Medical Diagnosis

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Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

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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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Acknowledgment

This work was done while the first author did a research internship at Aillis Inc., Japan. We would like to thank all researchers, specially doctor Sho Okiyama, Memori Fukuda, Kazutaka Okuda for their valuable comments and feedback.

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Authors and Affiliations

  1. Aillis Inc., Tokyo, Japan

    Quan Huu Cap & Atsushi Fukuda

  2. Graduate School of Science and Engineering, Hosei University, Tokyo, Japan

    Quan Huu Cap & Hitoshi Iyatomi

Authors
  1. Quan Huu Cap
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  2. Hitoshi Iyatomi
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  3. Atsushi Fukuda
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Corresponding author

Correspondence to Quan Huu Cap .

Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

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Cap, Q.H., Iyatomi, H., Fukuda, A. (2021). MIINet: An Image Quality Improvement Framework for Supporting Medical Diagnosis. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12661. Springer, Cham. https://doi.org/10.1007/978-3-030-68763-2_19

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  • DOI: https://doi.org/10.1007/978-3-030-68763-2_19

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