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

TY - GEN

T1 - GANReDL

T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

AU - Liu, Pan

AU - Li, Chao

AU - Schönlieb, Carola Bibiane

N1 - Publisher Copyright: © 2019, Springer Nature Switzerland AG.

PY - 2019/10/10

Y1 - 2019/10/10

N2 - 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.

AB - 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.

KW - Generative adversarial networks

KW - Medical image super-resolution

KW - Real order derivative operators

UR - https://www.scopus.com/pages/publications/85075646560

U2 - 10.1007/978-3-030-32248-9_13

DO - 10.1007/978-3-030-32248-9_13

M3 - Conference contribution

AN - SCOPUS:85075646560

SN - 9783030322472

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 110

EP - 117

BT - Medical Image Computing and Computer Assisted Intervention –

A2 - Shen, Dinggang

A2 - Yap, Pew-Thian

A2 - Liu, Tianming

A2 - Peters, Terry M.

A2 - Khan, Ali

A2 - Staib, Lawrence H.

A2 - Essert, Caroline

A2 - Zhou, Sean

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 13 October 2019 through 17 October 2019

ER -