Abstract
Deep learning-based classification of rare anemia disorders is challenged by the lack of training data and instance-level annotations. Multiple Instance Learning (MIL) has shown to be an effective solution, yet it suffers from low accuracy and limited explainability. Although the inclusion of attention mechanisms has addressed these issues, their effectiveness highly depends on the amount and diversity of cells in the training samples. Consequently, the poor machine learning performance on rare anemia disorder classification from blood samples remains unresolved. In this paper, we propose an interpretable pooling method for MIL to address these limitations. By benefiting from instance-level information of negative bags (i.e., homogeneous benign cells from healthy individuals), our approach increases the contribution of anomalous instances. We show that our strategy outperforms standard MIL classification algorithms and provides a meaningful explanation behind its decisions. Moreover, it can denote anomalous instances of rare blood diseases that are not seen during the training phase.
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Acknowledgements
The Helmholtz Association supports the present contribution under the joint research school “Munich School for Data Science - MUDS”. C.M. has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 866411). CoMMiTMenT study was funded by the European Seventh Framework Program under grant agreement number 602121 (CoMMiTMenT) and from the European Union’s Horizon 2020 Research and Innovation Programme. MemSID (NCT02615847) clinical trial was funded by the Foundation for Clinical Research Hematology for supporting the clinical trail at the Division of Hematology, University Hospital Zurich, and, partially, by the following foundations: Baugarten Zürich Genossenschaft und Stiftung, the Ernst Goehner Stiftung, the René und Susanna Braginsky Stiftung, the Stiftung Symphasis and the Botnar Foundation.” Further funding for analysis of the obtained data was obtained European Union’s Horizon 2020 Research and Innovation Programme under grant agreement number 675115-RELEVANCE-H2020-MSCA-ITN-2015/H2020-MSCA-ITN-2015.
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Kazeminia, S., Sadafi, A., Makhro, A., Bogdanova, A., Albarqouni, S., Marr, C. (2022). Anomaly-Aware Multiple Instance Learning for Rare Anemia Disorder Classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13438. Springer, Cham. https://doi.org/10.1007/978-3-031-16452-1_33
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