Multi-Task Multi-Domain Learning for Digital Staining and Classification of Leukocytes

Tomczak A, Ilic S, Marquardt G, Engel T, Forster F, Navab N, Albarqouni S (2021)

Publication Type: Journal article

Publication year: 2021

Journal

IEEE Transactions on Medical Imaging Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 40

Pages Range: 2897-2910

Journal Issue: 10

DOI: 10.1109/TMI.2020.3046334

Abstract

This paper addresses digital staining and classification of the unstained white blood cell images obtained with a differential contrast microscope. We have data coming from multiple domains that are partially labeled and partially matching across the domains. Using unstained images removes time-consuming staining procedures and could facilitate and automatize comprehensive diagnostics. To this aim, we propose a method that translates unstained images to realistically looking stained images preserving the inter-cellular structures, crucial for the medical experts to perform classification. We achieve better structure preservation by adding auxiliary tasks of segmentation and direct reconstruction. Segmentation enforces that the network learns to generate correct nucleus and cytoplasm shape, while direct reconstruction enforces reliable translation between the matching images across domains. Besides, we build a robust domain agnostic latent space by injecting the target domain label directly to the generator, i.e., bypassing the encoder. It allows the encoder to extract features independently of the target domain and enables an automated domain invariant classification of the white blood cells. We validated our method on a large dataset composed of leukocytes of 24 patients, achieving state-of-The-Art performance on both digital staining and classification tasks.

Involved external institutions

Siemens Healthineers

Germany (DE) Johns Hopkins University (JHU)

United States (USA) (US)

How to cite

APA:

Tomczak, A., Ilic, S., Marquardt, G., Engel, T., Forster, F., Navab, N., & Albarqouni, S. (2021). Multi-Task Multi-Domain Learning for Digital Staining and Classification of Leukocytes. IEEE Transactions on Medical Imaging, 40(10), 2897-2910. https://dx.doi.org/10.1109/TMI.2020.3046334

MLA:

Tomczak, Agnieszka, et al. "Multi-Task Multi-Domain Learning for Digital Staining and Classification of Leukocytes." IEEE Transactions on Medical Imaging 40.10 (2021): 2897-2910.

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