Histopathological Image Analysis in Medical Decision Making

2019 ◽  
pp. 139-153
Author(s):  
R. Meena Prakash ◽  
Shantha Selva Kumari R.
Keyword(s):  
Digital Pathology ◽  
Medical Decision ◽  
Heterogeneous Structure ◽  
Nuclei Segmentation ◽  
Histopathological Image ◽  
Inter Observer Variability ◽  
Computer Aided Analysis ◽  
Histopathological Images ◽  
Severity Of The Disease

Digital pathology is one of the significant methods in the medicine field to diagnose and treat cancer. The cell morphology and architecture distribution of biopsies are analyzed to diagnose the spread and severity of the disease. Manual analyses are time-consuming and subjected to intra- and inter-observer variability. Digital pathology and computer-aided analysis aids in enormous applications including nuclei detection, segmentation, and classification. The major challenges in nuclei segmentation are high variability in images due to differences in preparation of slides, heterogeneous structure, overlapping clusters, artifacts, and noise. The structure of the proposed chapter is as follows. First, an introduction about digital pathology and significance of digital pathology techniques in cancer diagnosis based on literature survey is given. Then, the method of classification of histopathological images using deep learning for different datasets is proposed with experimental results.

scholarly journals Extracting Time-Frequency Features of Images for Robust LSTM-based Classification of H&E Stained Tissue

2020 ◽  
Author(s):  
Tuan Pham
Keyword(s):  
Short Term Memory ◽  
Digital Pathology ◽  
Support Vector ◽  
Automated Classification ◽  
Time Frequency ◽  
Statistical Measures ◽  
Vector Machines ◽  
Histopathological Images ◽  
Frequency Features

The importance of automated classification of histopathological images has been increasingly recognized for effective processing of large volumes of data in the era of digital pathology for new discovery of disease mechanism. This paper presents a deep-learning approach that extracts time-frequency features of H&E stained tissue images for classification by long short-term memory networks. Using two large public databases of colorectal-cancer and heart-failure H&E stained tissue images, the proposed approach outperforms several state-of-the-art benchmark classification methods, including support vector machines and convolutional neural networks in terms of several statistical measures.

scholarly journals A Review of Breast Cancer Histopathological Image Classification

2021 ◽  
Vol 2129 (1) ◽  
pp. 012049
Author(s):  
Lei Huang ◽  
Azlan Mohd Zain ◽  
Kai-Qing Zhou ◽  
Chang-Feng Chen
Keyword(s):  
Breast Cancer ◽  
Image Classification ◽  
Histopathological Examination ◽  
Breast Cancer Diagnosis ◽  
Advantages And Disadvantages ◽  
Histopathological Image ◽  
Histopathological Images ◽  
Classification Of Images ◽  
Histopathological Image Classification

Abstract Breast Cancer (BC) is the most common malignant tumor for women in the world. Histopathological examination serves as basis for breast cancer diagnosis. Due to the low accuracy of histopathological images through manual judgment, the classification of histopathological images of breast cancer has become a research hotspot in the field of medical image processing. Accurate classification of images can help doctors to properly diagnoses and improve the survival rate of patients. This paper reviews the existing works on histopathological image classification of breast cancer and analysis the advantages and disadvantages of related algorithms. Findings of the histopathological image classification of the Breast Cancer study are drawn, and the possible future directions are also discussed.

scholarly journals Classification enhancement of breast cancer histopathological image using penalized logistic regression

2019 ◽  
Vol 13 (1) ◽  
pp. 405
Author(s):  
Mohammed Abdulrazaq Kahya
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Classification Accuracy ◽  
Area Under The Curve ◽  
Classification Performance ◽  
Histopathological Image ◽  
Classification Of Diseases ◽  
Histopathological Images ◽  
Penalized Logistic Regression

<p>Classification of breast cancer histopathological images plays a significant role in computer-aided diagnosis system. Features matrix was extracted in order to classify those images and they may contain outlier values adversely that affect the classification performance. Smoothing of features matrix has been proved to be an effective way to improve the classification result via eliminating of outlier values. In this paper, an adaptive penalized logistic regression is proposed, with the aim of smoothing features and provides high classification accuracy of histopathological images, by combining the penalized logistic regression with the smoothed features matrix. Experimental results based on a publicly recent breast cancer histopathological image datasets show that the proposed method significantly outperforms penalized logistic regression in terms of classification accuracy and area under the curve. Thus, the proposed method can be useful for histopathological images classification and other classification of diseases types using DNA gene expression data in the real clinical practice.</p>

scholarly journals Extracting Time-Frequency Features of Images for Robust LSTM-based Classification of H&E Stained Tissue

2020 ◽  
Author(s):  
Tuan Pham
Keyword(s):  
Short Term Memory ◽  
Digital Pathology ◽  
Support Vector ◽  
Automated Classification ◽  
Time Frequency ◽  
Statistical Measures ◽  
Vector Machines ◽  
Histopathological Images ◽  
Frequency Features

The importance of automated classification of histopathological images has been increasingly recognized for effective processing of large volumes of data in the era of digital pathology for new discovery of disease mechanism. This paper presents a deep-learning approach that extracts time-frequency features of H&E stained tissue images for classification by long short-term memory networks. Using two large public databases of colorectal-cancer and heart-failure H&E stained tissue images, the proposed approach outperforms several state-of-the-art benchmark classification methods, including support vector machines and convolutional neural networks in terms of several statistical measures.

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

scholarly journals Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Image Analysis ◽  
Short Term Memory ◽  
Disease Diagnosis ◽  
Short Term ◽  
Term Memory ◽  
Time Frequency ◽  
Time Space ◽  
Histopathological Images ◽  
Long Short Term Memory

AbstractImage analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.

scholarly journals Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2419
Author(s):  
Georg Steinbuss ◽  
Mark Kriegsmann ◽  
Christiane Zgorzelski ◽  
Alexander Brobeil ◽  
Benjamin Goeppert ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hodgkin Lymphoma ◽  
Expert Knowledge ◽  
B Cell Lymphoma ◽  
High Accuracy ◽  
Lymphocytic Leukemia ◽  
Non Hodgkin Lymphoma ◽  
Image Patches ◽  
Histopathological Images

The diagnosis and the subtyping of non-Hodgkin lymphoma (NHL) are challenging and require expert knowledge, great experience, thorough morphological analysis, and often additional expensive immunohistological and molecular methods. As these requirements are not always available, supplemental methods supporting morphological-based decision making and potentially entity subtyping are required. Deep learning methods have been shown to classify histopathological images with high accuracy, but data on NHL subtyping are limited. After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference lymph nodes, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia, and nodal diffuse large B-cell lymphoma. The optimized algorithm achieved an accuracy of 95.56% on an independent test set including 16,960 image patches from 125 patients after the application of quality controls. Automatic classification of NHL is possible with high accuracy using deep learning on histopathological images and routine diagnostic applications should be pursued.

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