Abstract
The detection of mitotic figures in histological tumor images plays a vital role in the decision-making of the appropriate therapy. However, tissue preparation and image acquisition methods degrade the performances of the deep learning-based approaches for mitotic figures detection. MIDOG challenge addresses the domain-shift problem of this detection task. In an endeavour to reduce this domain shift, we propose a pre-processing autoencoder that is trained adversarially to the sources of domain variations. The output of this autoencoder, exhibiting a uniform domain appearance, is finally given as input to the retina-net based mitosis detection module.
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References
Aubreville, M., et al.: Mitosis domain generalization challenge. Zenodo (2021). https://doi.org/10.5281/zenodo.4573978
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180–1189. PMLR (2015)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Marzahl, C., et al.: Deep learning-based quantification of pulmonary hemosiderophages in cytology slides. Sci. Rep. 10(1), 1–10 (2020)
Smith, L.N.: Cyclical learning rates for training neural networks. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 464–472. IEEE (2017)
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Almahfouz Nasser, S., Kurian, N.C., Sethi, A. (2022). Domain Generalisation for Mitosis Detection Exploting Preprocessing Homogenizers. In: Aubreville, M., Zimmerer, D., Heinrich, M. (eds) Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis. MICCAI 2021. Lecture Notes in Computer Science(), vol 13166. Springer, Cham. https://doi.org/10.1007/978-3-030-97281-3_12
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DOI: https://doi.org/10.1007/978-3-030-97281-3_12
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