scholarly journals Noise reduction in optical coherence tomography images using a deep neural network with perceptually-sensitive loss function

2020 ◽  
Vol 11 (2) ◽  
pp. 817 ◽  
Author(s):  
Bin Qiu ◽  
Zhiyu Huang ◽  
Xi Liu ◽  
Xiangxi Meng ◽  
Yunfei You ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography ◽  
Noise Reduction ◽  
Loss Function ◽  
Deep Neural Network ◽  
Optical Coherence

Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network

2021 ◽  
Vol 137 ◽  
pp. 106861
Author(s):  
Deepa Joshi ◽  
Ankit Butola ◽  
Sheetal Raosaheb Kanade ◽  
Dilip K. Prasad ◽  
S.V. Amitha Mithra ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography ◽  
Deep Neural Network ◽  
Optical Coherence ◽  
Label Free ◽  
Non Invasive ◽  
Rice Seeds

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 Multiple-cardiac-cycle noise reduction in dynamic optical coherence tomography of the embryonic heart and vasculature

2009 ◽  
Vol 34 (23) ◽  
pp. 3704 ◽  
Author(s):  
Sandeep Bhat ◽  
Irina V. Larina ◽  
Kirill V. Larin ◽  
Mary E. Dickinson ◽  
Michael Liebling
Keyword(s):  
Optical Coherence Tomography ◽  
Noise Reduction ◽  
Cardiac Cycle ◽  
Embryonic Heart ◽  
Optical Coherence
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