Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images

2016 ◽  
Vol 35 (8) ◽  
pp. 1962-1971 ◽  
Author(s):  
Abhishek Vahadane ◽  
Tingying Peng ◽  
Amit Sethi ◽  
Shadi Albarqouni ◽  
Lichao Wang ◽  
...  
Keyword(s):  
Structure Preserving ◽  
Histological Images

Fully Automatic Blind Color Deconvolution of Histological Images Using Super Gaussians

2021 ◽  
Author(s):  
Fernando Perez-Bueno ◽  
Miguel Vega ◽  
Valery Naranjo ◽  
Rafael Molina ◽  
Aggelos K. Katsaggelos
Keyword(s):  
Color Deconvolution ◽  
Histological Images ◽  
Fully Automatic

Classification of papillary thyroid carcinoma histological images based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Yaning Liu ◽  
Lin Han ◽  
Hexiang Wang ◽  
Bo Yin
Keyword(s):  
Neural Network ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Image Features ◽  
Papillary Thyroid ◽  
Histological Image ◽  
Histological Images

Papillary thyroid carcinoma (PTC) is a common carcinoma in thyroid. As many benign thyroid nodules have the papillary structure which could easily be confused with PTC in morphology. Thus, pathologists have to take a lot of time on differential diagnosis of PTC besides personal diagnostic experience and there is no doubt that it is subjective and difficult to obtain consistency among observers. To address this issue, we applied deep learning to the differential diagnosis of PTC and proposed a histological image classification method for PTC based on the Inception Residual convolutional neural network (IRCNN) and support vector machine (SVM). First, in order to expand the dataset and solve the problem of histological image color inconsistency, a pre-processing module was constructed that included color transfer and mirror transform. Then, to alleviate overfitting of the deep learning model, we optimized the convolution neural network by combining Inception Network and Residual Network to extract image features. Finally, the SVM was trained via image features extracted by IRCNN to perform the classification task. Experimental results show effectiveness of the proposed method in the classification of PTC histological images.

A novel transfer learning approach for the classification of histological images of colorectal cancer

2021 ◽  
Author(s):  
Elene Firmeza Ohata ◽  
João Victor Souza das Chagas ◽  
Gabriel Maia Bezerra ◽  
Mohammad Mehedi Hassan ◽  
Victor Hugo Costa de Albuquerque ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Transfer Learning ◽  
Learning Approach ◽  
Histological Images

Structure-preserving NPR framework for image abstraction and stylization

2021 ◽  
Author(s):  
M. P. Pavan Kumar ◽  
B. Poornima ◽  
H. S. Nagendraswamy ◽  
C. Manjunath
Keyword(s):  
Structure Preserving ◽  
Image Abstraction

Model reduction in power systems using a structure-preserving balanced truncation approach

2019 ◽  
Vol 177 ◽  
pp. 106002
Author(s):  
Johnny Leung ◽  
Michel Kinnaert ◽  
Jean-Claude Maun ◽  
Fortunato Villella
Keyword(s):  
Power Systems ◽  
Model Reduction ◽  
Balanced Truncation ◽  
Structure Preserving

scholarly journals Structure-preserving retargeting of irregular 3D architecture

2011 ◽  
Author(s):  
Jinjie Lin ◽  
Daniel Cohen-Or ◽  
Hao Zhang ◽  
Cheng Liang ◽  
Andrei Sharf ◽  
...  
Keyword(s):  
Structure Preserving ◽  
3D Architecture
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