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arXiv · 2007.11641

Attention based Multiple Instance Learning for Classification of Blood Cell Disorders

Ario Sadafi·Asya Makhro·Anna Bogdanova·Nassir Navab·Tingying Peng·Shadi Albarqouni·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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Ario Sadafi, Asya Makhro, Anna Bogdanova, Nassir Navab, Tingying Peng, Shadi Albarqouni, Carsten Marr. 2020-07-22. Attention based Multiple Instance Learning for Classification of Blood Cell Disorders. https://arxiv.org/abs/2007.11641

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