  Red blood cells are highly deformable and present in various shapes. In blood
cell disorders, only a subset of all cells is morphologically altered and
relevant for the diagnosis. However, manually labeling of all cells is
laborious, complicated and introduces inter-expert variability. We propose an
attention based multiple instance learning method to classify blood samples of
patients suffering from blood cell disorders. Cells are detected using an R-CNN
architecture. With the features extracted for each cell, a multiple instance
learning method classifies patient samples into one out of four blood cell
disorders. The attention mechanism provides a measure of the contribution of
each cell to the overall classification and significantly improves the
network's classification accuracy as well as its interpretability for the
medical expert.
