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2019 | OriginalPaper | Buchkapitel

A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement

verfasst von : Uddeshya Upadhyay, Suyash P. Awate

Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

Verlag: Springer International Publishing

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Abstract

Deep neural networks for image quality enhancement typically need large quantities of highly-curated training data comprising pairs of low-quality images and their corresponding high-quality images. While high-quality image acquisition is typically expensive and time-consuming, medium-quality images are faster to acquire, at lower equipment costs, and available in larger quantities. Thus, we propose a novel generative adversarial network (GAN) that can leverage training data at multiple levels of quality (e.g., high and medium quality) to improve performance while limiting costs of data curation. We apply our mixed-supervision GAN to (i) super-resolve histopathology images and (ii) enhance laparoscopy images by combining super-resolution and surgical smoke removal. Results on large clinical and pre-clinical datasets show the benefits of our mixed-supervision GAN over the state of the art.

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Vorheriges Kapitel Generalized Non-rigid Point Set Registration with Hybrid Mixture Models Considering Anisotropic Positional Uncertainties

Nächstes Kapitel Combined Learning for Similar Tasks with Domain-Switching Networks

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Titel: A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement
verfasst von: Uddeshya Upadhyay
Suyash P. Awate
Verlag: Springer International Publishing
Buch: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
Print ISBN: 978-3-030-32253-3

Electronic ISBN: 978-3-030-32254-0

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-32254-0_62

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