AIMC Topic: Breast Neoplasms

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A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation.

Journal of biomedical informatics
Breast cancer is the most common cause of cancer death in women. Today, post-transcriptional protein products of the genes involved in breast cancer can be identified by immunohistochemistry. However, this method has problems arising from the intra-o...

Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model.

Scientific reports
Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcino...

AutoIHC-scoring: a machine learning framework for automated Allred scoring of molecular expression in ER- and PR-stained breast cancer tissue.

Journal of microscopy
In prognostic evaluation of breast cancer Immunohistochemical (IHC) markers namely, oestrogen receptor (ER) and progesterone receptor (PR) are widely used. The expert pathologist investigates qualitatively the stained tissue slide under microscope to...

Integrated gene expression profiling and chromatin immunoprecipitation followed by sequencing: Analysis of the C-terminal binding protein in breast cancer.

The journal of obstetrics and gynaecology research
AIM: This study explored the possible mechanisms of the transcriptional regulatory activities of C-terminal binding protein (CtBP) and the role of CtBP in the pathogenesis of breast cancer.

An Advanced Deep Learning Approach for Ki-67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer.

Scientific reports
Being a non-histone protein, Ki-67 is one of the essential biomarkers for the immunohistochemical assessment of proliferation rate in breast cancer screening and grading. The Ki-67 signature is always sensitive to radiotherapy and chemotherapy. Due t...

Classification of breast cancer histology images using Convolutional Neural Networks.

PloS one
Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contrib...

Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach.

Scientific reports
Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differ...

Metastasis detection from whole slide images using local features and random forests.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in cos...

Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection.

Computers in biology and medicine
Different types of breast cancer are affecting lives of women across the world. Common types include Ductal carcinoma in situ (DCIS), Invasive ductal carcinoma (IDC), Tubular carcinoma, Medullary carcinoma, and Invasive lobular carcinoma (ILC). While...

Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. ...