Dataset used is available at this repository.
The image dataset is composed of high-resolution (2040 × 1536 pixels), uncompressed, and annotated H&E stain images from the Bioimaging 2015 breast histology classification challenge.
All the images are digitized with the same acquisition conditions, with magnification of 200× and pixel size of 0.42μm × 0.42μm. Each image is labeled with one of four classes: i) normal tissue, ii) benign lesion, iii) in situ carcinoma and iv) invasive carcinoma
Patches of size 512 x 512 are extracted with a stride of 256. Before training, the pixels values are normalized and centered.
In this notebook, there's a Keras implementation of the patch-wise network of the paper Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification:
@inproceedings{nazeri2018two,
title={Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification},
author={Nazeri, Kamyar and Aminpour, Azad and Ebrahimi, Mehran},
booktitle={International Conference Image Analysis and Recognition},
pages={717--726},
year={2018},
organization={Springer}
}