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Training sample selection based on self-training for liver cirrhosis classification using ultrasound images.

, , , , , and . QCAV, volume 10338 of SPIE Proceedings, page 103380J. SPIE, (2017)

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A note of liver cirrhosis classification on M-mode ultrasound images by higher-order local auto-correlation features., , , , , , , and . SoCPaR, page 50-53. IEEE, (2013)Development of a Gastric Cancer Diagnostic Support System with a Pattern Recognition Method Using a Hyperspectral Camera., , , , , and . J. Sensors, (2016)Training sample selection based on self-training for liver cirrhosis classification using ultrasound images., , , , , and . QCAV, volume 10338 of SPIE Proceedings, page 103380J. SPIE, (2017)Training ROI Selection Based on MILBoost for Liver Cirrhosis Classification Using Ultrasound Images., , , , , and . IEA/AIE, volume 9799 of Lecture Notes in Computer Science, page 451-459. Springer, (2016)An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images., , , , , , and . J. Medical Syst., 41 (8): 119:1-119:9 (2017)A Method of Bubble Removal for Computer-Assisted Diagnosis of Capsule Endoscopic Images., , , , , and . IEA/AIE (2), volume 8482 of Lecture Notes in Computer Science, page 228-233. Springer, (2014)An Improved Method for Cirrhosis Detection Using Liver's Ultrasound Images., , , , and . ICPR, page 2294-2297. IEEE Computer Society, (2010)Image Correction Methods for Regions of Interest in Liver Cirrhosis Classification on CNNs., , , , and . Sensors, 22 (9): 3378 (2022)