GANReDL: Medical Image Enhancement Using a Generative Adversarial Network with Real-Order Derivative Induced Loss Functions

Pan Liu (Lead / Corresponding author), Chao Li, Carola Bibiane Schönlieb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Deep (convolutional) neural networks (DCNN) have recently gained popularity, and shown improved performance in the field of image enhancement (de-noising and super-resolution, for instance). However, the central issue of recovering finer texture details in images still remains unsolved. State-of-the-art objective functions used in DCNN mostly focus on minimizing the mean squared reconstruction error. The resulting image estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details, and are therefore error-prone with respect to fine-scale, possibly clinically relevant details. In this article, we present GANReDL, a generative adversarial network (GAN) for image enhancement equipped with a real-order derivative induced loss functions (ReDL) which we will show gives improved images, in particular wrt to the reconstruction of fine-scale details. To the best of our knowledge, this is the first framework that incorporates non-integer order derivatives in loss functions. To this aim, we propose a discriminator network that is trained to differentiate between the enhanced images and ground-truth images, and propose a new loss function motivated by real-order derivatives which is capable of also capturing global image features rather than pixel-wise features only. We show, with several numerical experiments, that GANReDL is better in reconstructing the high-frequency image details, and therefore show improved performance for image enhancement over other state-of-the-art methods.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention –
Subtitle of host publicationMICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer Science and Business Media Deutschland GmbH
Pages110-117
Number of pages8
ISBN (Electronic)9783030322489
ISBN (Print)9783030322472
DOIs
Publication statusPublished - 10 Oct 2019
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 13 Oct 201917 Oct 2019
https://www.miccai2019.org/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11766 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/TerritoryChina
CityShenzhen
Period13/10/1917/10/19
Internet address

Keywords

  • Generative adversarial networks
  • Medical image super-resolution
  • Real order derivative operators

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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