Histopathological images are the gold standard for breast cancer diagnosis. During
examination several dozens of them are acquired for a single patient. Conventional, image …

The performance of most conventional classification systems relies on appropriate data
representation and much of the efforts are dedicated to feature engineering, a difficult and …

Today, medical image analysis papers require solid experiments to prove the usefulness of
proposed methods. However, experiments are often performed on data selected by the …

Breast cancer (BC) is a deadly disease, killing millions of people every year. Developing
automated malignant BC detection system applied on patient's imagery can help dealing …

There are several breast cancer datasets for building Computer Aided Diagnosis systems
(CADs) using either deep learning or traditional models. However, most of these datasets …

Mahotas is a computer vision library for Python. It contains traditional image processing
functionality such as filtering and morphological operations as well as more modern …

Computer-aided diagnosis (CAD) systems of breast cancer histopathological images
automated classification can help reduce the manual observation workload of pathologists …

A huge amount of important biomedical information is hidden in the bulk of research articles
in biomedical fields. At the same time, the publication of databases of biological information …

Several researchers are trying to develop different computer‐aided diagnosis system for
breast cancer employing machine learning (ML) methods. The inputs to these ML algorithms …

In this paper we address the problem of automated grading of invasive breast carcinoma
through the encoding of histological images as VLAD (Vector of Locally Aggregated …