论文标题
利用深度学习系统在多模式视网膜扫描中的可转移性来提取视网膜病变
Exploiting the Transferability of Deep Learning Systems Across Multi-modal Retinal Scans for Extracting Retinopathy Lesions
论文作者
论文摘要
视网膜病变在视网膜异常的准确分类中起着至关重要的作用。许多研究人员提出了深层病变感知的筛查系统，以分析和分级视网膜病的发展。但是，据我们所知，没有文献利用这些系统趋于跨越多种扫描仪规范和多模式图像的趋势。为此，本文介绍了语义分割，场景解析和混合深度学习系统的详细评估，用于提取视网膜病变，例如视网膜内流体，视网膜下液体，硬渗出液，DRUSEN和其他脉络化脉络化异常，并从融合金的素和光含量和光学相干性的相干性整体术（OCT）（OCT）Imagraphy（Oct）Imagraphy（ICT）成像中。此外，我们提出了一种新的策略，该策略利用了这些模型在多个视网膜扫描仪规范中的可传递性。在这项研究中，使用了七个公开数据集的363枚眼底和173,915个10月扫描（从中使用了297枚眼底和59,593次10月扫描来进行测试目的）。总体而言，通过Resnet-50进行了回封的杂种视网膜分析和分级网络（RAGNET），首先是提取视网膜病变，达到平均骰子系数得分为0.822。此外，完整的源代码及其文档将在以下网址发布：http：//biomisa.org/index.php/downloads/。
Retinal lesions play a vital role in the accurate classification of retinal abnormalities. Many researchers have proposed deep lesion-aware screening systems that analyze and grade the progression of retinopathy. However, to the best of our knowledge, no literature exploits the tendency of these systems to generalize across multiple scanner specifications and multi-modal imagery. Towards this end, this paper presents a detailed evaluation of semantic segmentation, scene parsing and hybrid deep learning systems for extracting the retinal lesions such as intra-retinal fluid, sub-retinal fluid, hard exudates, drusen, and other chorioretinal anomalies from fused fundus and optical coherence tomography (OCT) imagery. Furthermore, we present a novel strategy exploiting the transferability of these models across multiple retinal scanner specifications. A total of 363 fundus and 173,915 OCT scans from seven publicly available datasets were used in this research (from which 297 fundus and 59,593 OCT scans were used for testing purposes). Overall, a hybrid retinal analysis and grading network (RAGNet), backboned through ResNet-50, stood first for extracting the retinal lesions, achieving a mean dice coefficient score of 0.822. Moreover, the complete source code and its documentation are released at: http://biomisa.org/index.php/downloads/.
