Attention based Multiple Instance Learning for Classification of Blood Cell Disorders

Authors:
  • ASArio Sadafi
    Ario Sadafi
  • AMAsya Makhro
    Asya Makhro
  • ABAnna Bogdanova
    Anna Bogdanova
  • NNNassir Navab
    Nassir Navab
  • TPTingying Peng
    Tingying Peng
  • SAShadi Albarqouni
    Shadi Albarqouni
  • CMCarsten Marr
    Carsten Marr
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Abstract
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.
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