scholarly journals Feedback GMDH-type Neural Network and Its Application to Medical Image Analysis of Liver Cancer

2011 ◽  
Vol 2011 (0) ◽  
pp. 256-263
Author(s):  
Tadashi Kondo ◽  
Junji Ueno
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Liver Cancer ◽  
Medical Image ◽  
Medical Image Analysis

Medical image analysis of abdominal X-ray CT images by deep multi-layered GMDH-type neural network

2017 ◽  
Vol 23 (2) ◽  
pp. 271-278
Author(s):  
Shoichiro Takao ◽  
Sayaka Kondo ◽  
Junji Ueno ◽  
Tadashi Kondo
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Ct Images ◽  
X Ray

scholarly journals An integrated iterative annotation technique for easing neural network training in medical image analysis

2019 ◽  
Vol 1 (2) ◽  
pp. 112-119 ◽  
Author(s):  
Brendon Lutnick ◽  
Brandon Ginley ◽  
Darshana Govind ◽  
Sean D. McGarry ◽  
Peter S. LaViolette ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Neural Network Training ◽  
Network Training

Medical Image Segmentation based on U-Net: A Review

2020 ◽  
Vol 64 (2) ◽  
pp. 20508-1-20508-12 ◽  
Author(s):  
Getao Du ◽  
Xu Cao ◽  
Jimin Liang ◽  
Xueli Chen ◽  
Yonghua Zhan
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Image Segmentation ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Imaging Systems

Abstract Medical image analysis is performed by analyzing images obtained by medical imaging systems to solve clinical problems. The purpose is to extract effective information and improve the level of clinical diagnosis. In recent years, automatic segmentation based on deep learning (DL) methods has been widely used, where a neural network can automatically learn image features, which is in sharp contrast with the traditional manual learning method. U-net is one of the most important semantic segmentation frameworks for a convolutional neural network (CNN). It is widely used in the medical image analysis domain for lesion segmentation, anatomical segmentation, and classification. The advantage of this network framework is that it can not only accurately segment the desired feature target and effectively process and objectively evaluate medical images but also help to improve accuracy in the diagnosis by medical images. Therefore, this article presents a literature review of medical image segmentation based on U-net, focusing on the successful segmentation experience of U-net for different lesion regions in six medical imaging systems. Along with the latest advances in DL, this article introduces the method of combining the original U-net architecture with deep learning and a method for improving the U-net network.

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