Weak Supervision in Convolutional Neural Network for Semantic Segmentation of Diffuse Lung Diseases Using Partially Annotated Dataset

• URL: http://arxiv.org/abs/2002.11936v2
• Date: Thu, 26 Mar 2020 11:04:49 GMT
• Title: Weak Supervision in Convolutional Neural Network for Semantic Segmentation of Diffuse Lung Diseases Using Partially Annotated Dataset
• Authors: Yuki Suzuki, Kazuki Yamagata, Yanagawa Masahiro, Shoji Kido, Noriyuki Tomiyama
• Abstract summary: We develop semantic segmentation model for 5 kinds of lung diseases. DLDs considered in this work are consolidation, ground glass opacity, honeycombing, emphysema, and normal. We propose a new weak supervision technique that effectively utilizes partially annotated dataset.
• Score: 2.239917051803692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computer-aided diagnosis system for diffuse lung diseases (DLDs) is necessary for the objective assessment of the lung diseases. In this paper, we develop semantic segmentation model for 5 kinds of DLDs. DLDs considered in this work are consolidation, ground glass opacity, honeycombing, emphysema, and normal. Convolutional neural network (CNN) is one of the most promising technique for semantic segmentation among machine learning algorithms. While creating annotated dataset for semantic segmentation is laborious and time consuming, creating partially annotated dataset, in which only one chosen class is annotated for each image, is easier since annotators only need to focus on one class at a time during the annotation task. In this paper, we propose a new weak supervision technique that effectively utilizes partially annotated dataset. The experiments using partially annotated dataset composed 372 CT images demonstrated that our proposed technique significantly improved segmentation accuracy.

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