Application of U-Shaped Convolutional Neural Network Based on Attention Mechanism in Liver CT Image Segmentation

2020 ◽  
pp. 198-206
Author(s):  
Chen Li ◽  
Wei Chen ◽  
Xin Luo ◽  
Mingfei Wu ◽  
Xiaogang Jia ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Attention Mechanism ◽  
Ct Image ◽  
Liver Ct

scholarly journals CT image segmentation of bone for medical additive manufacturing using a convolutional neural network

2018 ◽  
Vol 103 ◽  
pp. 130-139 ◽  
Author(s):  
Jordi Minnema ◽  
Maureen van Eijnatten ◽  
Wouter Kouw ◽  
Faruk Diblen ◽  
Adriënne Mendrik ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Additive Manufacturing ◽  
Convolutional Neural Network ◽  
Ct Image

Dynamic adaptive residual network for liver CT image segmentation

2021 ◽  
Vol 91 ◽  
pp. 107024 ◽  
Author(s):  
Xiwang Xie ◽  
Weidong Zhang ◽  
Huadeng Wang ◽  
Lingqiao Li ◽  
Zhengyun Feng ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Ct Image ◽  
Residual Network ◽  
Liver Ct

scholarly journals Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Peter M. Maloca ◽  
Philipp L. Müller ◽  
Aaron Y. Lee ◽  
Adnan Tufail ◽  
Konstantinos Balaskas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image

AbstractMachine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization (‘neural recording’). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications.

scholarly journals Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Segmentation Method ◽  
Semantic Image Segmentation ◽  
Batch Normalization ◽  
Set Up ◽  
Image Datasets

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

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