Predicting Breast Cancer using histopathology images

Predicting Breast Cancer using histopathology images

Predicting Breast Cancer using histopathology images. Breast cancer is the most common form of cancer in women, and invasive ductal carcinoma (IDC) is the most common form of breast cancer. The goal of this article is to identify IDC when it is present in otherwise unlabeled histopathology images.

Breast cancer is the most common form of cancer in women, and invasive ductal carcinoma (IDC) is the most common form of breast cancer. Accurately identifying and categorizing breast cancer subtypes is an important clinical task, and automated methods can be used to save time and reduce errors.

The goal of this article is to identify IDC when it is present in otherwise unlabeled histopathology images.

The dataset consists of 277,524 50x50 pixel RGB digital image patches that were derived from 162 H&E-stained breast histopathology samples. These images are small patches that were extracted from digital images of breast tissue samples.

The breast tissue contains many cells but only some of them are cancerous. Patches that are labeled “1” contain cells that are characteristic of invasive ductal carcinoma. For more information about the data, see https://www.ncbi.nlm.nih.gov/pubmed/27563488 and http://spie.org/Publications/Proceedings/Paper/10.1117/12.2043872.

Dataset Download Link: https://www.kaggle.com/paultimothymooney/breast-histopathology-images

Let’s start working on the dataset.

machine-learning data-science deep-learning cancer python

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