Task-GAN: Improving Generative Adversarial Network for Image Reconstruction
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal Volume
11905 LNCS
Pages
193-204
Date Issued
2019
Author(s)
Abstract
Generative Adversarial Network (GAN) has demonstrated great potentials in computer vision tasks such as image restoration. However, image restoration for specific scenarios, such as medical image enhancement is still facing challenge: How to ensure the visually plausible results while not containing hallucinated features that might jeopardize downstream tasks such as pathology identification? Here, we propose Task-GAN, a generalized model for medical reconstruction problem. A task-specific network that captures the diagnostic/pathology features, was added to couple the GAN based image reconstruction framework. Validated on multiple medical datasets, we demonstrated that the proposed method leads to improved deep learning based image reconstruction while preserving the detailed structure and diagnostic features. ? Springer Nature Switzerland AG 2019.
Subjects
Computer aided instruction
Deep learning
Diagnosis
Image enhancement
Machine learning
Medical image processing
Restoration
Adversarial networks
Diagnostic features
GaN based
Generalized models
Medical data sets
Reconstruction frameworks
Reconstruction problems
Image reconstruction
SDGs
Type
conference paper