The goal of this project is the quantification of pulmonary hemosiderophages in cytology slides with deep learning based object detection methods.

Key features:

1) Pulmonary emosiderophage classification with human comparable performance

2) Explanatory and reproducible cell classification outcomes

3) RetinaNet based object detection in less than two minutes for whole slide images (40.000 x 40.000 Pixel)