论文标题
使用多尺度的深度特征稀疏编码在视网膜图像中的异常检测
Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding
论文作者
论文摘要
卷积神经网络模型已从光学相干断层扫描（OCT）和眼底图像中成功检测到视网膜疾病。这些CNN模型经常依靠大量的标记数据进行训练，尤其是对于罕见疾病而言。此外，在只有一种或几种疾病的数据集中训练的深度学习系统无法检测到其他疾病，从而限制了该系统在疾病鉴定中的实际使用。我们引入了一种无监督的方法来检测视网膜图像中的异常，以克服此问题。我们提出了一种简单，有效，易于训练的方法，该方法遵循多步训练技术，该技术结合了自动编码器训练和多规模深度功能稀疏编码（MDFSC），这是普通稀疏编码的扩展版本，以容纳多种类型的视网膜数据集。在Eye-Q，IDRID和八粒数据集上，我们的AUC分数提高了7.8％，6.7 \％和12.1 \％。
Convolutional Neural Network models have successfully detected retinal illness from optical coherence tomography (OCT) and fundus images. These CNN models frequently rely on vast amounts of labeled data for training, difficult to obtain, especially for rare diseases. Furthermore, a deep learning system trained on a data set with only one or a few diseases cannot detect other diseases, limiting the system's practical use in disease identification. We have introduced an unsupervised approach for detecting anomalies in retinal images to overcome this issue. We have proposed a simple, memory efficient, easy to train method which followed a multi-step training technique that incorporated autoencoder training and Multi-Scale Deep Feature Sparse Coding (MDFSC), an extended version of normal sparse coding, to accommodate diverse types of retinal datasets. We achieve relative AUC score improvement of 7.8\%, 6.7\% and 12.1\% over state-of-the-art SPADE on Eye-Q, IDRiD and OCTID datasets respectively.
