论文标题
使用联合注意网络提高鲁棒性，以检测光学相干断层扫描图像的视网膜变性
Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
论文作者
论文摘要
由不同的眼科病理引起的嘈杂数据和眼部外观的相似性对自动化专家系统对准确检测视网膜疾病提出了重大挑战。此外，缺乏知识转移性和对不合理的大数据集的需求限制了当前机器学习系统的临床应用。为了提高鲁棒性，需要更好地了解视网膜子空间变形如何导致各种水平的疾病严重程度来确定优先级特定疾病的模型细节。在本文中，我们建议将特异性特征代表形式用作一个由两个联合网络组成的新建结构 - 一种用于监督疾病模型的编码，另一个用于以无监督的方式产生注意图以保留特定疾病的空间信息。我们对公开数据集的实验结果表明，拟议的联合网络显着提高了看不见的数据集上最先进的视网膜疾病分类网络的准确性和鲁棒性。
Noisy data and the similarity in the ocular appearances caused by different ophthalmic pathologies pose significant challenges for an automated expert system to accurately detect retinal diseases. In addition, the lack of knowledge transferability and the need for unreasonably large datasets limit clinical application of current machine learning systems. To increase robustness, a better understanding of how the retinal subspace deformations lead to various levels of disease severity needs to be utilized for prioritizing disease-specific model details. In this paper we propose the use of disease-specific feature representation as a novel architecture comprised of two joint networks -- one for supervised encoding of disease model and the other for producing attention maps in an unsupervised manner to retain disease specific spatial information. Our experimental results on publicly available datasets show the proposed joint-network significantly improves the accuracy and robustness of state-of-the-art retinal disease classification networks on unseen datasets.
