Attention based Multiple Instance Learning for Classification of Blood Cell Disorders
This addresses the challenge of laborious manual labeling and inter-expert variability in diagnosing blood cell disorders, representing an incremental improvement in medical image analysis.
The paper tackled the problem of classifying blood cell disorders by using an attention-based multiple instance learning method to identify relevant cells in patient samples, which significantly improved classification accuracy and interpretability.
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.