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 # ICIAR2018
+### Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification
 This repository is the part A of the ICIAR 2018 Grand Challenge on BreAst Cancer Histology (BACH) images for automatically classifying H&E stained breast histology microscopy images in four classes: normal, benign, in situ carcinoma and invasive carcinoma. 
 
 We are presenting a CNN approach using two convolutional networks to classify histology images in a patchwise fashion. The first network, receives overlapping patches (35 patches) of the whole-slide image and learns to generate spatially smaller outputs. The second network is trained on the downsampled patches of the whole image using the output of the first network. The number of channels in the input to the second network is equal to the total number of patches extracted from the microscopy image in a non-overlapping fashion (12 patches) times the depth of the feature maps generted by the first network (C):