计算机科学
判别式
人工智能
分割
模式识别(心理学)
注释
分类器(UML)
基本事实
监督学习
深度学习
人工神经网络
作者
Amrest Chinkamol,Vetit Kanjaras,Phattarapong Sawangjai,Yitian Zhao,Thapanun Sudhawiyangkul,Chantana Chantrapornchai,Cuntai Guan,Theerawit Wilaiprasitporn
出处
期刊:Cornell University - arXiv
日期:2022-07-25
标识
DOI:10.48550/arxiv.2207.12238
摘要
While there have been increased researches using deep learning techniques for the extraction of vascular structure from the 2D en face OCTA, for such approach, it is known that the data annotation process on the curvilinear structure like the retinal vasculature is very costly and time consuming, albeit few tried to address the annotation problem. In this work, we propose the application of the scribble-base weakly-supervised learning method to automate the pixel-level annotation. The proposed method, called OCTAve, combines the weakly-supervised learning using scribble-annotated ground truth augmented with an adversarial and a novel self-supervised deep supervision. Our novel mechanism is designed to utilize the discriminative outputs from the discrimination layer of a UNet-like architecture where the Kullback-Liebler Divergence between the aggregate discriminative outputs and the segmentation map predicate is minimized during the training. This combined method leads to the better localization of the vascular structure as shown in our experiments. We validate our proposed method on the large public datasets i.e., ROSE, OCTA-500. The segmentation performance is compared against both state-of-the-art fully-supervised and scribble-based weakly-supervised approaches. The implementation of our work used in the experiments is located at [LINK].
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