scholarly journals Optical coherence tomography image denoising using a generative adversarial network with speckle modulation

2020 ◽  
Vol 13 (4) ◽  
Author(s):  
Zhao Dong ◽  
Guoyan Liu ◽  
Guangming Ni ◽  
Jason Jerwick ◽  
Lian Duan ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Image Denoising ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Tomography Image

Optical Coherence Tomography Image Denoising Algorithm Based on Wavelet Transform and Fractional Integral

2019 ◽  
Vol 56 (18) ◽  
pp. 181008
Author(s):  
张晨曦 Chenxi Zhang ◽  
陈明惠 Minghui Chen ◽  
王帆 Fan Wang ◽  
高乃珺 Naijun Gao ◽  
郑刚 Gang Zheng
Keyword(s):  
Optical Coherence Tomography ◽  
Wavelet Transform ◽  
Image Denoising ◽  
Fractional Integral ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image

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.

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