Breast cancer grading plays an important role in predicting the aggressiveness of the disease. A key component in breast cancer grading is mitosis count (quantifying the number of cells in the process of diving at a given time). Currently, pathologists in the labs manually detect and count mitosis.

The goal of this project is to bring the power of machine learning to the field of pathology and provide a consistent tool and diagnostic aid that relieves pathologists of this tedious task.

In this project, I leverage digital pathology images and convolutional neural networks to learn features of cells undergoing mitosis and detect them.

This approach detects mitotic cells and non-mitotic cells that might be indistinguishable to the human eye.

This approach attempts to handle staining variation across different samples

ICPR 2014 Contest
This contest is supported in part by the French National Research Agency ANR, project MICO under reference
ANR-10-TECS-015, and by the Fondation AVEC (Association Vivons Ensemble avec le Cancer)

Look at code/README