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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
To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast  ...  Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers.  ...  In [106] , a context-aware network for automated classification of breast cancer histopathological images is proposed.  ... 
arXiv:2003.12255v2 fatcat:dghl3hszhrb7zlidym5z2x3mvq

Deep Object Detection based Mitosis Analysis in Breast Cancer Histopathological Images [article]

Anabia Sohail, Muhammad Ahsan Mukhtar, Asifullah Khan, Muhammad Mohsin Zafar, Aneela Zameer, Saranjam Khan
2020 arXiv   pre-print
This work proposes an end-to-end detection system for mitotic nuclei identification in breast cancer histopathological images.  ...  Empirical evaluation of breast tissue biopsies for mitotic nuclei detection is considered an important prognostic biomarker in tumor grading and cancer progression.  ...  Acknowledgements The authors thank Higher Education Commission of Pakistan (HEC) for granting funds under HEC indigenous scholarship program and Pattern Recognition lab at DCIS, PIEAS, for providing computational  ... 
arXiv:2003.08803v1 fatcat:bryz622fnba5bkgmyqpcapkxby

Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review [article]

Subrato Bharati, Prajoy Podder, M. Rubaiyat Hossain Mondal
2020 arXiv   pre-print
For predicting breast cancer, several automated systems are already developed using different medical imaging modalities.  ...  Breast cancer is a common fatal disease for women. Early diagnosis and detection is necessary in order to improve the prognosis of breast cancer affected people.  ...  Chen J, Qin Z, "Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features", Neurocomputing, Vol. 229, pp.34-44, 2017. doi: 10.1016/j.neucom  ... 
arXiv:2006.01767v1 fatcat:jjy3d2mgabfrrnpbkbyskfb2pi

A Cascade Deep Forest Model for Breast Cancer Subtype Classification Using Multi-Omics Data

Ala'a El-Nabawy, Nahla A. Belal, Nashwa El-Bendary
2021 Mathematics  
Breast cancer data, especially biological data, is known for its imbalance, with lack of extensive amounts of histopathological images as biological data.  ...  Automated diagnosis systems aim to reduce the cost of diagnosis while maintaining the same efficiency. Many methods have been used for breast cancer subtype classification.  ...  cascade Deep Forest-based model for breast cancer subtype classification using multi-omics data. • Obtain comparable results using only omics data without using histopathological images. • Improve the  ... 
doi:10.3390/math9131574 fatcat:4b5perv7ifaqpcbnpqyqau4sga

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  
To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast  ...  Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers.  ...  In [105] , a context-aware network for automated classification of breast cancer histopathological images is proposed.  ... 
doi:10.1109/access.2020.2993788 fatcat:33e54jp6hzggtozlngxvotcpsu

Integrated diagnostics: a conceptual framework with examples

Anant Madabhushi, Scott Doyle, George Lee, Ajay Basavanhally, James Monaco, Steve Masters, John Tomaszewski, Michael Feldman
2010 Clinical Chemistry and Laboratory Medicine  
(choice of therapy) predictions from high resolution images of digitized histopathology.  ...  One of the caveats to developing image analysis algorithms for digitized histopathology is the ability to deal with highly dense, information rich datasets; datasets that would overwhelm most computer  ...  R01CA136535-01, R21CA127186, R03CA128081-01, and R03CA143991-01, The Cancer Institute of New Jersey, Life Science Commercialization Award from Rutgers University.  ... 
doi:10.1515/cclm.2010.193 pmid:20491597 fatcat:q2dseyj7ivgu7pndbtspni5354

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
Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis.  ...  One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems.  ...  An Adaboost ensemble is used by Romo-Bucheli et al. [119] to grade skin cancer. The ensemble classifies images described by features created with graph theory to represent the nuclei distribution.  ... 
arXiv:2102.03889v1 fatcat:ylrsildl4nenho22erndvpjcjy

Deep neural network models for computational histopathology: A survey [article]

Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
2019 arXiv   pre-print
In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis.  ...  Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to diseases progression and patient survival outcomes.  ...  Automated grading of gliomas using deep learning in digital pathology images: A modular approach with ensemble of convolutional neural networks, in: AMIA Annual Symposium Proceedings, p. 1899.  ... 
arXiv:1912.12378v1 fatcat:xdfkzzwzb5alhjfhffqpcurb2u

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

Sarah M. Ayyad, Mohamed Shehata, Ahmed Shalaby, Mohamed Abou El-Ghar, Mohammed Ghazal, Moumen El-Melegy, Nahla B. Abdel-Hamid, Labib M. Labib, H. Arafat Ali, Ayman El-Baz
2021 Sensors  
However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images.  ...  Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes  ...  Conflicts of Interest: The authors declare no conflict of interest. Sensors 2021, 21, 2586  ... 
doi:10.3390/s21082586 pmid:33917035 fatcat:qfspvswivrbnlaih5y4gun5zwm

Histopathologic Image Processing: A Review [article]

Jonathan de Matos, Alceu de Souza Britto Jr., Luiz E. S. Oliveira, Alessandro L. Koerich
2019 arXiv   pre-print
We also bring a study case of breast cancer classification using a mix of deep and shallow machine learning methods.  ...  Histopathologic Images (HI) are the gold standard for evaluation of some tumors.  ...  It was based on transfer learning from one dataset of colorectal histopathology images to other of breast cancer.  ... 
arXiv:1904.07900v1 fatcat:7pd7jgrvfvamtdzagh6lexo3l4

Cancer diagnosis through a tandem of classifiers for digitized histopathological slides

Daniel Lichtblau, Catalin Stoean, Marco Magalhaes
2019 PLoS ONE  
The current research study is concerned with the automated differentiation between histopathological slides from colon tissues with respect to four classes (healthy tissue and cancerous of grades 1, 2  ...  or 3) through an optimized ensemble of predictors.  ...  Recently, a large data set of breast cancer histopathology images acquired from 82 patients was introduced in [9] under the name BreaKHis.  ... 
doi:10.1371/journal.pone.0209274 pmid:30650087 pmcid:PMC6334911 fatcat:ygl7vt3665dmtesujqn6xvh64e

Deep learning in cancer diagnosis, prognosis and treatment selection

Khoa A. Tran, Olga Kondrashova, Andrew Bradley, Elizabeth D. Williams, John V. Pearson, Nicola Waddell
2021 Genome Medicine  
analysis of the complex biology of cancer.  ...  The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the  ...  Acknowledgements Khoa Tran was the recipient of the Maureen and Barry Stevenson PhD Scholarship, we are grateful to Maureen Stevenson for her support.  ... 
doi:10.1186/s13073-021-00968-x pmid:34579788 pmcid:PMC8477474 fatcat:y73fumwdazft3pw47gqdcncnue

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  
Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis.  ...  One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems.  ...  An Adaboost ensemble is used by Romo-Bucheli et al. [118] to grade skin cancer. The ensemble classifies images described by features created with graph theory to represent the nuclei distribution.  ... 
doi:10.3390/electronics10050562 fatcat:7kkj7qncxvdhdm3qvliwcqm3tq

A generalized deep learning framework for whole-slide image segmentation and analysis

Mahendra Khened, Avinash Kori, Haran Rajkumar, Ganapathy Krishnamurthi, Balaji Srinivasan
2021 Scientific Reports  
We demonstrate the efficacy and improved generalizability of our framework by evaluating it on a variety of histopathology tasks including breast cancer metastases (CAMELYON), colon cancer (DigestPath)  ...  However, deep learning-based solutions pose many technical challenges, including the large size of WSI data, heterogeneity in images, and complexity of features.  ...  In order to make the diagnostic and grading task of tumors less subjective, quantifiable features are derived from the images that correlate with the condition of the disease 1 .  ... 
doi:10.1038/s41598-021-90444-8 pmid:34078928 fatcat:xbg5bfa4arer3hyz2q5bonm7fy

Translational AI and Deep Learning in Diagnostic Pathology

Ahmed Serag, Adrian Ion-Margineanu, Hammad Qureshi, Ryan McMillan, Marie-Judith Saint Martin, Jim Diamond, Paul O'Reilly, Peter Hamilton
2019 Frontiers in Medicine  
The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI.  ...  This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology.  ...  The method proved useful in discriminating breast cancer metastases with different pathologic stages from digital breast histopathological images.  ... 
doi:10.3389/fmed.2019.00185 pmid:31632973 pmcid:PMC6779702 fatcat:jd5arv2lc5aq3lbk7ztipt5mca
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