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

Li, Chen; Dong, Xue; Hu, Zhijie; Chen, Hao; Yao, Yu-Dong; Zhang, Yong; Li, Mo; Wang, Qian; Xu, Ning

doi:10.1007/978-3-030-23762-2_20

Cited by 13 publications

(10 citation statements)

References 48 publications

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“…It poses a serious risk to women's health and can spread through direct or distant metastasis [1]. In developing countries, it is the second most prevalent malignancy after breast cancer and the third dominant cause of cancer-related deaths after breast and lung cancer [2]. Moreover, developing countries are more vulnerable to prevent cancer deaths because of a lack of awareness and adequate medical facilities, which leads to nearly 90% of cervical cancer-related deaths [3].…”

Section: Introductionmentioning

confidence: 99%

An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification

et al. 2020

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In recent years, researches are concentrating on the effectiveness of Transfer Learning (TL) and Ensemble Learning (EL) techniques in cervical histopathology image analysis. However, there have been very few investigations that have described the stages of differentiation of cervical histopathological images. Therefore, in this article, we propose an Ensembled Transfer Learning (ETL) framework to classify well, moderate and poorly differentiated cervical histopathological images. First of all, we have developed Inception-V3, Xception, VGG-16, and Resnet-50 based TL structures. Then, to enhance the classification performance, a weighted voting based EL strategy is introduced. After that, to evaluate the proposed algorithm, a dataset consisting of 307 images, stained by three immunohistochemistry methods (AQP, HIF, and VEGF) is considered. In the experiment, we obtain the highest overall accuracy of 97.03% and 98.61% on AQP staining images and poor differentiation of VEGF staining images, individually. Finally, an additional experiment for classifying the benign cells from the malignant ones is carried out on the Herlev dataset and obtains an overall accuracy of 98.37%.

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Section: Introductionmentioning

confidence: 99%

An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification

et al. 2020

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“…The motivation is to clarify the development history of ANNs, understand the popular technology and trend of ANN applications, and discover the future potential of ANNs in the BHIA field. As far as we know, there exist some survey papers that summarize papers related to the BHIA work (e.g., the reviews in [7], [9], [12], [17]- [33]). In the following part, we go through the survey papers that are related to the BHIA work.…”

Section: B Motivation Of Our Review Papermentioning

confidence: 99%

“…In our previous work [33], we propose a brief survey for breast histopathology image analysis using classical and deep neural networks. With more than 60 related works, referring to classical ANNs, deep ANNs and methodology analysis.…”

Section: B Motivation Of Our Review Papermentioning

confidence: 99%

“…Besides our previous brief review about BHIA with ANN techniques, there is not a special one that focuses on the ANN approaches in this field. Hence, in order to clarify the BHIA work using ANN approaches in recent years, as of early 2020, based on our previous work in [33], we summarize more than 150 related works to prepare this comprehensive review. We propose this paper with the following structure: In Sec.…”

Section: B Motivation Of Our Review Papermentioning

confidence: 99%

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

et al. 2020

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Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. 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 histopathological images. 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. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

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“…Similar to human beings, ML algorithms encompass a period of “training”, in which they learn a predictive model, and a period of “validation”, where the model is applied to never-before-seen cases. In the medical field, several tools have already been implemented: notable examples are the interpretation of radiological [ 7 , 8 ], histopathological [ 9 , 10 ], and fundus oculi images [ 11 , 12 ], as well as the prediction of clinical outcomes based on electronic health records [ 13 ]. As FC produces a large amount of data, whose interpretation needs high analytical skills and a conspicuous experience, it naturally represents the perfect field of application for ML algorithms: accordingly, ML tools have already been applied to different FC steps, from data preprocessing [ 14 , 15 ] to disease or minimal residual disease (MRD) detection [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Clinically Applicable Approach to the Classification of B-Cell Non-Hodgkin Lymphomas with Flow Cytometry and Machine Learning

Gaidano

Tenace

Santoro

et al. 2020

Cancers

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The immunophenotype is a key element to classify B-cell Non-Hodgkin Lymphomas (B-NHL); while it is routinely obtained through immunohistochemistry, the use of flow cytometry (FC) could bear several advantages. However, few FC laboratories can rely on a long-standing practical experience, and the literature in support is still limited; as a result, the use of FC is generally restricted to the analysis of lymphomas with bone marrow or peripheral blood involvement. In this work, we applied machine learning to our database of 1465 B-NHL samples from different sources, building four artificial predictive systems which could classify B-NHL in up to nine of the most common clinico-pathological entities. Our best model shows an overall accuracy of 92.68%, a mean sensitivity of 88.54% and a mean specificity of 98.77%. Beyond the clinical applicability, our models demonstrate (i) the strong discriminatory power of MIB1 and Bcl2, whose integration in the predictive model significantly increased the performance of the algorithm; (ii) the potential usefulness of some non-canonical markers in categorizing B-NHL; and (iii) that FC markers should not be described as strictly positive or negative according to fixed thresholds, but they rather correlate with different B-NHL depending on their level of expression.

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

Cited by 13 publications

References 48 publications

An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification

An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification

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

A Clinically Applicable Approach to the Classification of B-Cell Non-Hodgkin Lymphomas with Flow Cytometry and Machine Learning

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