# Attention based Multiple Instance Learning for Classification of Blood Cell Disorders Source: [Attention based Multiple Instance Learning for Classification of Blood Cell Disorders]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:03:45.482Z Authors: Ario Sadafi, Asya Makhro, Anna Bogdanova, Nassir Navab, Tingying Peng, Shadi Albarqouni, Carsten Marr Published: 2020-07-22T19:29:40.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Classification on Rare Anemia Disorders (SCD, Thal, Xero, HS, Control) (3-fold cross-validation)](): Classification; Rare Anemia Disorders (SCD, Thal, Xero, HS, Control) (3-fold cross-validation); Accuracy: 77. ## Original sources and implementation [Original paper]() [PDF]()