Abstract
Deep learning based medical image segmentation models need to be robust to domain shifts and image distortion for the safe translation of these models into clinical practice. The most popular methods for improving robustness are centred around data augmentation and adversarial training. Many image segmentation tasks exhibit regular structures with only limited variability. We aim to exploit this notion by learning a set of base components in the latent space whose composition can account for the entire structural variability of a specific segmentation task. We enforce a hierarchical prior in the composition of the base components and consider the natural geometry in which to build our hierarchy. Specifically, we embed the base components on a hyperbolic manifold which we claim leads to a more natural composition. We demonstrate that our method improves model robustness under various perturbations and in the task of single domain generalisation.
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Acknowledgements
This work was supported and funded by Cancer Research UK (CRUK) (C309/A28804).
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Santhirasekaram, A., Winkler, M., Rockall, A., Glocker, B. (2024). Hierarchical Compositionality in Hyperbolic Space for Robust Medical Image Segmentation. In: Koch, L., et al. Domain Adaptation and Representation Transfer. DART 2023. Lecture Notes in Computer Science, vol 14293. Springer, Cham. https://doi.org/10.1007/978-3-031-45857-6_6
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DOI: https://doi.org/10.1007/978-3-031-45857-6_6
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