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Accurately predicting the risk of cancer recurrence and metastasis is critical to cancer individualized treatment. Currently, physicians commonly use histological grade, which is determined by pathologists via performing a semi-quantitative analysis of three histological and cytological features on Hematoxylin-Eosin (HE) stained histopathological images, to assess the prognosis of a breast cancer...
HER-2/neu, a protein often giving higher aggressiveness in breast cancers, has been shown that if the gene is expanded for some reason, the Her-2 protein produced by the cells will be over-expressed to enhance the cancer cells reproduced ability, the prognosis will be also relatively less, too. The HER-2 immunohistochemical stained provides a simple and reliable method for pathologist in clinical...
Breast cancer is the most common cancer diagnosed among U.S. women. In this paper we have done some experiments for tumor detection in digital mammogram images. First of all, we have described a method that segments the breast image automatically. As a preprocessing, we have used fuzzy based noise removal filter that removes noise. Then for segmentation, we have provided a background removal method...
Mass in mammogram can be an indicator of breast cancer. In this work we propose a new approach using twin support vector machine (TWSVM) for automated detection of mass in digital mammograms. This algorithm finds two hyperplanes to classify data points into different classes according to the relevance between a given point and either plane. It works much faster than original SVM classifier. The proposed...
Triple-negative (TN) breast cancer has gained much interest recently due to its lack of response to receptor-targeted therapies and its aggressive clinical nature. In this study, we evaluate the ability of a computer-aided diagnosis (CAD) system to not only distinguish benign from malignant lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), but also to quantitatively distinguish...
The current gold standard for predicting disease survival and outcome for lymph node-negative, estrogen receptor-positive breast cancer (LN-, ER+ BC) patients is via the gene-expression based assay, Oncotype DX. In this paper, we present a novel computer-aided prognosis (CAP) scheme that employs quantitatively derived image information to predict patient outcome analogous to the Oncotype DX recurrence...
A mainstay in cancer diagnostics is the classification or grading of cell nuclei based on their appearance. While the analysis of cytological samples has been automated successfully for a long time, the complexity of histological tissue samples has prevented a reliable classification with machine vision techniques. We approach this complex problem in multiple stages, analyzing first image quality,...
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