Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification
Salehi R, Sadafi A, Gruber A, Lienemann P, Navab N, Albarqouni S, Marr C (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13433 LNCS
Pages Range: 739-748
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Singapore, SGP
ISBN: 9783031164361
DOI: 10.1007/978-3-031-16437-8_71
Abstract
Diagnosing hematological malignancies requires identification and classification of white blood cells in peripheral blood smears. Domain shifts caused by different lab procedures, staining, illumination, and microscope settings hamper the re-usability of recently developed machine learning methods on data collected from different sites. Here, we propose a cross-domain adapted autoencoder to extract features in an unsupervised manner on three different datasets of single white blood cells scanned from peripheral blood smears. The autoencoder is based on an R-CNN architecture allowing it to focus on the relevant white blood cell and eliminate artifacts in the image. To evaluate the quality of the extracted features we use a simple random forest to classify single cells. We show that thanks to the rich features extracted by the autoencoder trained on only one of the datasets, the random forest classifier performs satisfactorily on the unseen datasets, and outperforms published oracle networks in the cross-domain task. Our results suggest the possibility of employing this unsupervised approach in more complicated diagnosis and prognosis tasks without the need to add expensive expert labels to unseen data.
Involved external institutions
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
Universitätsklinikum Bonn
Germany (DE)
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
Universitätsklinikum Bonn
Germany (DE)
How to cite
APA:
Salehi, R., Sadafi, A., Gruber, A., Lienemann, P., Navab, N., Albarqouni, S., & Marr, C. (2022). Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification. In Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 739-748). Singapore, SGP: Springer Science and Business Media Deutschland GmbH.
MLA:
Salehi, Raheleh, et al. "Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification." Proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, SGP Ed. Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li, Springer Science and Business Media Deutschland GmbH, 2022. 739-748.
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