Intellectual software for analysis of histological images

2011 ◽  
Author(s):  
Alexander Nedzved ◽  
Valery Starovoitov
Keyword(s):  
Intellectual Software ◽  
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

scholarly journals Multi-center clinical study using optical coherence tomography for evaluation of cervical lesions in-vivo

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chenchen Ren ◽  
Xianxu Zeng ◽  
Zhongna Shi ◽  
Chunyan Wang ◽  
Huifen Wang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Real Time ◽  
Precancerous Lesions ◽  
Diagnostic Value ◽  
Optical Coherence ◽  
Cervical Lesions ◽  
In Vivo Evaluation ◽  
Histological Sections ◽  
Histological Images

AbstractIn this prospective study of an in-vivo cervical examination using optical coherence tomography (OCT), we evaluated the diagnostic value of non-invasive and real-time OCT in cervical precancerous lesions and cancer diagnosis, and determined the characteristics of OCT images. 733 patients from 5 Chinese hospitals were inspected with OCT and colposcopy-directed biopsy. The OCT images were compared with the histological sections to find out the characteristics of various categories of lesions. The OCT images were also interpreted by 3 investigators to make a 2-class classification, and the results were compared against the pathological results. Various structures of the cervical tissue were clearly observed in OCT images, which matched well with the corresponding histological sections. The OCT diagnosis results delivered a sensitivity of 87.0% (95% confidence interval, CI 82.2–90.7%), a specificity of 84.1% (95% CI 80.3–87.2%), and an overall accuracy of 85.1%. Both good consistency of OCT images and histological images and satisfactory diagnosis results were provided by OCT. Due to its features of non-invasion, real-time, and accuracy, OCT is valuable for the in-vivo evaluation of cervical lesions and has the potential to be one of the routine cervical diagnosis methods.

scholarly journals Computational method for unsupervised segmentation of lymphoma histological images based on fuzzy 3-partition entropy and genetic algorithm

2017 ◽  
Vol 81 ◽  
pp. 223-243 ◽  
Author(s):  
Thaína A. Azevedo Tosta ◽  
Paulo Rogério Faria ◽  
Leandro Alves Neves ◽  
Marcelo Zanchetta do Nascimento
Keyword(s):  
Genetic Algorithm ◽  
Computational Method ◽  
Unsupervised Segmentation ◽  
Histological Images

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

3D Localization of Neurons in Bright-Field Histological Images

2018 ◽  
Author(s):  
Andrija Stajduhar ◽  
Claude Lepage ◽  
Milos Judas ◽  
Sven Loncaric ◽  
Alan C. Evans
Keyword(s):  
Bright Field ◽  
3D Localization ◽  
Histological Images
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