Paper Info
Title
Sparse-Gan: Sparsity-Constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image
Abstract
With the development of convolutional neural network, deep learning has shown its success for retinal disease detection from optical coherence tomography (OCT) images. However, deep learning often relies on large scale labelled data for training, which is oftentimes challenging especially for disease with low occurrence. Moreover, a deep learning system trained from data-set with one or a few diseases is unable to detect other unseen diseases, which limits the practical usage of the system in disease screening. To address the limitation, we propose a novel anomaly detection framework termed Sparsity-constrained Generative Adversarial Network (Sparse-GAN) for disease screening where only healthy data are available in the training set. The contributions of Sparse-GAN are two-folds: 1) The proposed Sparse-GAN predicts the anomalies in latent space rather than image-level; 2) Sparse-GAN is constrained by a novel Sparsity Regularization Net. Furthermore, in light of the role of lesions for disease screening, we present to leverage on an anomaly activation map to show the heatmap of lesions. We evaluate our proposed Sparse-GAN on a publicly available dataset, and the results show that the proposed method outperforms the state-of-the-art methods.
Year
DOI
Venue
2020
10.1109/ISBI45749.2020.9098374
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
Keywords
DocType
ISSN
Anomaly Detection,Sparsity-constrained Network,Latent Feature,Adversarial Learning
Conference
1945-7928
ISBN
Citations 
PageRank 
978-1-5386-9331-5
4
0.39
References 
Authors
13
9
Name
Order
Citations
PageRank
Kang Zhou1247.82
Shenghua Gao2160766.89
Jun Cheng321420.65
Zaiwang Gu4855.88
Huazhu Fu5123565.07
Tu Zhi640.39
Jianlong Yang7184.01
Yitian Zhao8123.95
Jiang Liu929942.50