14 May 2017 Training sample selection based on self-training for liver cirrhosis classification using ultrasound images
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Proceedings Volume 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017; 103380J (2017) https://doi.org/10.1117/12.2264076
Event: The International Conference on Quality Control by Artificial Vision 2017, 2017, Tokyo, Japan
Abstract
Ultrasound imaging is a popular and non-invasive tool used in the diagnoses of liver disease. Cirrhosis is a chronic liver disease and it can advance to liver cancer. Early detection and appropriate treatment are crucial to prevent liver cancer. However, ultrasound image analysis is very challenging, because of the low signal-to-noise ratio of ultrasound images. To achieve the higher classification performance, selection of training regions of interest (ROIs) is very important that effect to classification accuracy. The purpose of our study is cirrhosis detection with high accuracy using liver ultrasound images. In our previous works, training ROI selection by MILBoost and multiple-ROI classification based on the product rule had been proposed, to achieve high classification performance. In this article, we propose self-training method to select training ROIs effectively. Evaluation experiments were performed to evaluate effect of self-training, using manually selected ROIs and also automatically selected ROIs. Experimental results show that self-training for manually selected ROIs achieved higher classification performance than other approaches, including our conventional methods. The manually ROI definition and sample selection are important to improve classification accuracy in cirrhosis detection using ultrasound images.
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Yusuke Fujita, Yoshihiro Mitani, Yoshihiko Hamamoto, Makoto Segawa, Shuji Terai, Isao Sakaida, "Training sample selection based on self-training for liver cirrhosis classification using ultrasound images ", Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103380J (14 May 2017); doi: 10.1117/12.2264076; https://doi.org/10.1117/12.2264076
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