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Automated mitosis detection in histopathology based on non-gaussian modeling of complex wavelet coefficients

Tao Wan, Wanshu Zhang, Min Zhu, Jianhui Chen, Alin Achim, Zengchang Qin
2017 Neurocomputing  
This study aims at improving the accuracy of automated mitosis detection by characterizing mitotic cells in wavelet based multi-resolution representations via a non-Gaussian modeling method.  ...  The inter-scale dependencies of wavelet coefficients allowing extraction of salient features within the cells that are more likely to appear at all different scales were captured by the bivariate non-Gaussian  ...  Acknowledgments This work was partially supported by the National Natural Science Foundation of China under award Nos. 61305047 and 61401012.  ... 
doi:10.1016/j.neucom.2017.01.008 fatcat:be3zcs3d4zftvdjd2tvkl4l3d4

Performance Evaluation of Cancer Diagnostics Using Autoregressive Features with SVM Classifier: Applications to Brain Cancer Histopathology

D. Vaishali, R. Ramesh, J. Anita Christaline
2016 International Journal of Multimedia and Ubiquitous Engineering  
In AR model, the parameters consist of a feature set of histopathological images obtained from biopsy samples taken from patients.  ...  These inferences are based on cell morphology and tissue distribution which represent randomness in growth and/or in placement.  ...  As the entire process of cell division occurs at random, the detection of mitosis becomes very complex.  ... 
doi:10.14257/ijmue.2016.11.6.21 fatcat:c2lmslrsvjaapa3saliy3f6khq

Quarter Plane ARMA Model for Analysis and Classification of Histopathology Images: Application to Cancer Detection

D. Vaishali, R. Ramesh, J. Anita Christaline
2016 Indian Journal of Science and Technology  
Pathologist detects random growth and random placements in tissue samples. These diagnostics are very subjective and based on experience/knowledge base of pathologists.  ...  This work presents the use of 2D Autoregressive And Moving Average (ARMA) model in computer assisted automatic cancer detection.  ...  Detection/study of uncontrolled mitosis becomes complex as entire process becomes random. 2 Detection and examination of mitotic figures in cancer screening is very important.  ... 
doi:10.17485/ijst/2016/v9i22/95299 fatcat:a4ve2czo6jb2pgqsvww7ez56kq

Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading

Gabriel Jiménez, Daniel Racoceanu
2019 Frontiers in Bioengineering and Biotechnology  
Focusing on a computer-based qualification for breast cancer diagnosis, the present study proposes two deep learning architectures to efficiently and effectively detect and classify mitosis in a histopathological  ...  Additionally, due to the semantic properties of the deep learning approach, an end-to-end deep learning framework is viable to perform both tasks: detection and classification of mitosis.  ...  AUTHOR CONTRIBUTIONS All authors contributed to the methodology proposal, analysis of results, writing, and review of the manuscript.  ... 
doi:10.3389/fbioe.2019.00145 pmid:31281813 pmcid:PMC6597878 fatcat:vym45szyanbhhafrjfno2vm3si

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Xiaomin Zhou, Chen Li, Md Mamunur Rahaman, Yudong Yao, Shiliang Ai, Changhao Sun, Qian Wang, Yong Zhang, Mo Li, Xiaoyan Li, Tao Jiang, Dan Xue (+2 others)
2020 IEEE Access  
In this review, we present a comprehensive overview of the BHIA techniques based on ANNs.  ...  First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented.  ...  In [155] , CNN based deep transfer learning is proposed to achieve automatic mitosis detection in breast histopathology images.  ... 
doi:10.1109/access.2020.2993788 fatcat:33e54jp6hzggtozlngxvotcpsu

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks [article]

Xiaomin Zhou, Chen Li, Md Mamunur Rahaman, Yudong Yao, Shiliang Ai, Changhao Sun, Xiaoyan Li, Qian Wang, Tao Jiang
2020 arXiv   pre-print
In this review, we present a comprehensive overview of the BHIA techniques based on ANNs.  ...  First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented.  ...  In [156] , CNN based deep transfer learning is proposed to achieve automatic mitosis detection in breast histopathology images.  ... 
arXiv:2003.12255v2 fatcat:dghl3hszhrb7zlidym5z2x3mvq

High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection

Angel Cruz-Roa, Hannah Gilmore, Ajay Basavanhally, Michael Feldman, Shridar Ganesan, Natalie Shih, John Tomaszewski, Anant Madabhushi, Fabio González, Yuanquan Wang
2018 PLoS ONE  
We applied HASHI to automated detection of invasive breast cancer on WSI.  ...  Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading.  ...  Overview of the high-throughput adaptive sampling for whole-slide histopathology images method (HASHI) based on CNNs for automated detection of invasive breast cancer (BCa) in WSIs.  ... 
doi:10.1371/journal.pone.0196828 pmid:29795581 pmcid:PMC5967747 fatcat:vubrn66qznhajneeohx4pj537u

A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

Xin Yu Liew, Nazia Hameed, Jeremie Clos
2021 Cancers  
This process identifies the presence of cancer in breast tissue samples and the distinct type of cancer stages.  ...  The aims are to investigate the impact of CAD systems using histopathology images, investigate deep learning methods that outperform conventional methods, and provide a summary for future researchers to  ...  On the same dataset, Wan et al. applied a dual-tree complex wavelet transform (DT-CWT) to describe the images in the context of mitosis detection in breast cancer and the generalized Gaussian distribution  ... 
doi:10.3390/cancers13112764 fatcat:vew6qmw2w5dgne3ocdfwggfflu

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Fuyong Xing, Lin Yang
2016 IEEE Reviews in Biomedical Engineering  
In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation.  ...  In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential  ...  A specific review on breast cancer histopathology image analysis is presented in [18] , which additionally covers mitosis detection and proliferation assessment.  ... 
doi:10.1109/rbme.2016.2515127 pmid:26742143 pmcid:PMC5233461 fatcat:hx5ldvsppvgzxk6rdiok7siyvi

Stain Deconvolution Using Statistical Analysis of Multi-Resolution Stain Colour Representation

Najah Alsubaie, Nicholas Trahearn, Shan E. Ahmed Raza, David Snead, Nasir M. Rajpoot, Cesario Bianchi
2017 PLoS ONE  
This approach statistically analyses the multi-resolutional representation of the image to separate the independent observations out of the correlated ones.  ...  Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms.  ...  We are also grateful to Korsuk Sirinukunwattana [2, 28] for providing the code of nuclei detection to be used in running the third experiment above. We would also like to thank Dr.  ... 
doi:10.1371/journal.pone.0169875 pmid:28076381 pmcid:PMC5226799 fatcat:xvkqgblpq5hgrcxmlwta3ebv74

Machine Learning Methods for Histopathological Image Analysis: A Review [article]

Jonathan de Matos and Steve Tsham Mpinda Ataky and Alceu de Souza Britto Jr. and Luiz Eduardo Soares de Oliveira and Alessandro Lameiras Koerich
2021 arXiv   pre-print
In this paper, we present a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods.  ...  One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems.  ...  [65] used a dual-tree complex wavelet transform (DT-CWT) to represent the images in the context of mitosis detection in breast cancer detection.  ... 
arXiv:2102.03889v1 fatcat:ylrsildl4nenho22erndvpjcjy

Machine Learning Methods for Histopathological Image Analysis: A Review

Jonathan de Matos, Steve Tsham Mpinda Ataky, Alceu de Souza Britto, Luiz Eduardo Soares de Oliveira, Alessandro Lameiras Koerich
2021 Electronics  
One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems.  ...  This paper presents a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods.  ...  [64] used a dual-tree complex wavelet transform (DT-CWT) to represent the images in the context of mitosis detection in breast cancer detection.  ... 
doi:10.3390/electronics10050562 fatcat:7kkj7qncxvdhdm3qvliwcqm3tq

Mining textural knowledge in biological images: Applications, methods and trends

Santa Di Cataldo, Elisa Ficarra
2017 Computational and Structural Biotechnology Journal  
This makes automated texture analysis fundamental in many applications of biomedicine, such as the accurate detection and grading of multiple types of cancer, the differential diagnosis of autoimmune diseases  ...  Indeed, visual textures can be exploited to distinguish specific tissues or cells in a biological sample, to highlight chemical reactions between molecules, as well as to detect subcellular patterns that  ...  Then, automated classification based on support vector machines (SVM [18] ) is used to distinguish between cancerous and non-cancerous images and to categorise the former ones into different grades of  ... 
doi:10.1016/j.csbj.2016.11.002 pmid:27994798 pmcid:PMC5155047 fatcat:kycs2tx2djef3jnvefpcg4hble

A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches [article]

Chen Li, Xintong Li, Md Rahaman, Xiaoyan Li, Hongzan Sun, Hong Zhang, Yong Zhang, Xiaoqi Li, Jian Wu, Yudong Yao, Marcin Grzegorzek
2021 arXiv   pre-print
This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced.  ...  Since 2004, WSI has been used more and more in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computers, they are highly efficient and labor-saving.  ...  The Gaussian pyramid is based on simple down-sampling plus Gaussian filtering.  ... 
arXiv:2102.10553v1 fatcat:ve4qkiwfjrb3fg7hal5uvpyxia

A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis [article]

Yixin Li, Chen Li, Xiaoyan Li, Kai Wang, Md Mamunur Rahaman, Changhao Sun, Hao Chen, Xinran Wu, Hong Zhang, Qian Wang
2021 arXiv   pre-print
In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models.  ...  To boost the accuracy and objectivity of detection, nowadays, an increasing number of computer-aided diagnosis (CAD) system is proposed.  ...  Guoxian Li for their importantsupport and discussion in this work.  ... 
arXiv:2009.13721v3 fatcat:q46wb3rhwjcode3b46h6v2lhoa
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