Attention based Multiple Instance Learning for Classification of Blood Cell Disorders

• URL: http://arxiv.org/abs/2007.11641v1
• Date: Wed, 22 Jul 2020 19:29:40 GMT
• Title: Attention based Multiple Instance Learning for Classification of Blood Cell Disorders
• Authors: Ario Sadafi, Asya Makhro, Anna Bogdanova, Nassir Navab, Tingying Peng, Shadi Albarqouni, Carsten Marr
• Abstract summary: We propose an attention based multiple instance learning method to classify blood samples of patients suffering from blood cell disorders. 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.
• Score: 38.086308180994976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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