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Machine Learning with Abstention for Automated Liver Disease Diagnosis
This paper presents a novel approach for detection of liver abnormalitie...
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Ultrasound Image Classification using ACGAN with Small Training Dataset
B-mode ultrasound imaging is a popular medical imaging technique. Like o...
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Multi-Modal Active Learning for Automatic Liver Fibrosis Diagnosis based on Ultrasound Shear Wave Elastography
With the development of radiomics, noninvasive diagnosis like ultrasound...
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Localization of Fetal Head in Ultrasound Images by Multiscale View and Deep Neural Networks
One of the routine examinations that are used for prenatal care in many ...
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Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning
Ultrasound imaging is a standard examination during pregnancy that can b...
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Deep learning in the ultrasound evaluation of neonatal respiratory status
Lung ultrasound imaging is reaching growing interest from the scientific...
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Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound
Accurate standard plane (SP) localization is the fundamental step for pr...
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Ultrasound Liver Fibrosis Diagnosis using Multi-indicator guided Deep Neural Networks
Accurate analysis of the fibrosis stage plays very important roles in follow-up of patients with chronic hepatitis B infection. In this paper, a deep learning framework is presented for automatically liver fibrosis prediction. On contrary of previous works, our approach can take use of the information provided by multiple ultrasound images. An indicator-guided learning mechanism is further proposed to ease the training of the proposed model. This follows the workflow of clinical diagnosis and make the prediction procedure interpretable. To support the training, a dataset is well-collected which contains the ultrasound videos/images, indicators and labels of 229 patients. As demonstrated in the experimental results, our proposed model shows its effectiveness by achieving the state-of-the-art performance, specifically, the accuracy is 65.6
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