Attention based Multiple Instance Learning for Classification of Blood Cell Disorders

TL;DR

This paper introduces an attention-based multiple instance learning approach that uses cell detection and feature extraction to classify blood disorders, improving accuracy and interpretability for medical diagnosis.

Contribution

It presents a novel combination of R-CNN based cell detection with attention-based multiple instance learning for blood disorder classification.

Findings

Enhanced classification accuracy with attention mechanism

Improved interpretability for medical experts

Effective detection and feature extraction of blood cells

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.