scholarly journals Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering

2017 ◽  
pp. 767-779 ◽  
Author(s):  
Shereen Fouad ◽  
David Randell ◽  
Antony Galton ◽  
Hisham Mehanna ◽  
Gabriel Landini
Keyword(s):  
Consensus Clustering ◽  
Histopathological Images

Consensus Clustering Using Weighted Association

2016 ◽  
Author(s):  
Aparajita Nanda ◽  
Arun K. Pujari
Keyword(s):  
Consensus Clustering

An Optimized Approach for Breast Cancer Classification for Histopathological Images Based on Hybrid Feature Set

2020 ◽  
Vol 16 ◽  
Author(s):  
Inzamam Mashood Nasir ◽  
Muhammad Rashid ◽  
Jamal Hussain Shah ◽  
Muhammad Sharif ◽  
Muhammad Yahiya Haider Awan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Classification Performance ◽  
Diagnose Breast Cancer ◽  
Histopathological Images ◽  
And Performance ◽  
Learned Features ◽  
Intelligent Healthcare

Background: Breast cancer is considered as the most perilous sickness among females worldwide and the ratio of new cases is expanding yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images. Objective: Most of these systems have either used traditional handcrafted features or deep features which had a lot of noise and redundancy, which ultimately decrease the performance of the system. Methods: A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pretrained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of proposed method. Results: The method concentrates on histopathological images to classify the breast cancer. The performance is compared with state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded. Conclusion: The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.

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 DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

2021 ◽  
Vol 28 ◽  
pp. 107327482098851
Author(s):  
Zeng-Hong Wu ◽  
Yun Tang ◽  
Yan Zhou
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Methylation Status ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Consensus Clustering ◽  
Breast Cancer Patients ◽  
Cancer Subtypes ◽  
Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

Nuclear Density Distribution Feature for Improving Cervical Histopathological Images Recognition

2021 ◽  
Author(s):  
Zhuangzhuang Wang ◽  
Mengning Yang ◽  
Yangfan Lyu ◽  
Kairun Chen ◽  
Qicheng Tang
Keyword(s):  
Density Distribution ◽  
Nuclear Density ◽  
Histopathological Images ◽  
Images Recognition

scholarly journals N6-Methyladenosine RNA modification in cerebrospinal fluid as a novel potential diagnostic biomarker for progressive multiple sclerosis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Fei Ye ◽  
Tianzhu Wang ◽  
Xiaoxin Wu ◽  
Jie Liang ◽  
Jiaoxing Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Sclerosis ◽  
Clustering Analysis ◽  
Roc Curves ◽  
Progressive Multiple Sclerosis ◽  
Rna Modification ◽  
Diagnostic Model ◽  
Consensus Clustering ◽  
Rna Methylation ◽  
M6a Rna Methylation

Abstract Background Progressive multiple sclerosis (PMS) is an uncommon and severe subtype of MS that worsens gradually and leads to irreversible disabilities in young adults. Currently, there are no applicable or reliable biomarkers to distinguish PMS from relapsing–remitting multiple sclerosis (RRMS). Previous studies have demonstrated that dysfunction of N6-methyladenosine (m6A) RNA modification is relevant to many neurological disorders. Thus, the aim of this study was to explore the diagnostic biomarkers for PMS based on m6A regulatory genes in the cerebrospinal fluid (CSF). Methods Gene expression matrices were downloaded from the ArrayExpress database. Then, we identified differentially expressed m6A regulatory genes between MS and non-MS patients. MS clusters were identified by consensus clustering analysis. Next, we analyzed the correlation between clusters and clinical characteristics. The random forest (RF) algorithm was applied to select key m6A-related genes. The support vector machine (SVM) was then used to construct a diagnostic gene signature. Receiver operating characteristic (ROC) curves were plotted to evaluate the accuracy of the diagnostic model. In addition, CSF samples from MS and non-MS patients were collected and used for external validation, as evaluated by an m6A RNA Methylation Quantification Kit and by real-time quantitative polymerase chain reaction. Results The 13 central m6A RNA methylation regulators were all upregulated in MS patients when compared with non-MS patients. Consensus clustering analysis identified two clusters, both of which were significantly associated with MS subtypes. Next, we divided 61 MS patients into a training set (n = 41) and a test set (n = 20). The RF algorithm identified eight feature genes, and the SVM method was successfully applied to construct a diagnostic model. ROC curves revealed good performance. Finally, the analysis of 11 CSF samples demonstrated that RRMS samples exhibited significantly higher levels of m6A RNA methylation and higher gene expression levels of m6A-related genes than PMS samples. Conclusions The dynamic modification of m6A RNA methylation is involved in the progression of MS and could potentially represent a novel CSF biomarker for diagnosing MS and distinguishing PMS from RRMS in the early stages of the disease.

scholarly journals Protein interactions and consensus clustering analysis uncover insights into herpesvirus virion structure and function relationships

PLoS Biology ◽  
2019 ◽  
Vol 17 (6) ◽  
pp. e3000316 ◽  
Author(s):  
Anna Hernández Durán ◽  
Todd M. Greco ◽  
Benjamin Vollmer ◽  
Ileana M. Cristea ◽  
Kay Grünewald ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Clustering Analysis ◽  
Structure And Function ◽  
Consensus Clustering ◽  
Virion Structure ◽  
And Function

Tumor Tissue Segmentation for Histopathological Images

2019 ◽  
Author(s):  
Xiansong Huang ◽  
Hongliang He ◽  
Pengxu Wei ◽  
Chi Zhang ◽  
Juncen Zhang ◽  
...  
Keyword(s):  
Tumor Tissue ◽  
Tissue Segmentation ◽  
Histopathological Images
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