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Polyp-artifact relationship analysis using graph inductive learned representations
arXiv - CS - Artificial Intelligence Pub Date : 2020-09-15 , DOI: arxiv-2009.07109
Roger D. Soberanis-Mukul, Shadi Albarqouni, Nassir Navab

The diagnosis process of colorectal cancer mainly focuses on the localization and characterization of abnormal growths in the colon tissue known as polyps. Despite recent advances in deep object localization, the localization of polyps remains challenging due to the similarities between tissues, and the high level of artifacts. Recent studies have shown the negative impact of the presence of artifacts in the polyp detection task, and have started to take them into account within the training process. However, the use of prior knowledge related to the spatial interaction of polyps and artifacts has not yet been considered. In this work, we incorporate artifact knowledge in a post-processing step. Our method models this task as an inductive graph representation learning problem, and is composed of training and inference steps. Detected bounding boxes around polyps and artifacts are considered as nodes connected by a defined criterion. The training step generates a node classifier with ground truth bounding boxes. In inference, we use this classifier to analyze a second graph, generated from artifact and polyp predictions given by region proposal networks. We evaluate how the choices in the connectivity and artifacts affect the performance of our method and show that it has the potential to reduce the false positives in the results of a region proposal network.

中文翻译:

使用图归纳学习表示法的息肉-伪影关系分析

大肠癌的诊断过程主要集中在结肠组织(称为息肉)中异常生长的定位和表征。尽管在深层物体定位方面取得了最新进展,但由于组织之间的相似性以及高水平的伪影,息肉的定位仍然具有挑战性。最近的研究表明,在息肉检测任务中存在伪影会带来负面影响,并已开始在训练过程中考虑到伪影。然而,尚未考虑使用与息肉和人工产物的空间相互作用有关的先验知识。在这项工作中,我们将人工制品知识纳入后处理步骤。我们的方法将此任务建模为归纳图表示学习问题,并且由训练和推理步骤组成。在息肉和伪影周围检测到的边界框被视为通过定义的标准连接的节点。训练步骤生成带有地面真值边界框的节点分类器。推断而言,我们使用该分类器来分析第二张图,该第二张图是根据区域提议网络给出的伪影和息肉预测生成的。我们评估了连通性和工件中的选择如何影响我们方法的性能,并表明它有可能减少区域提议网络结果中的误报。
更新日期:2020-09-16
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