unORANIC: Unsupervised Orthogonalization of Anatomy and Image-Characteristic Features

• URL: http://arxiv.org/abs/2308.15507v1
• Date: Tue, 29 Aug 2023 13:37:13 GMT
• Title: unORANIC: Unsupervised Orthogonalization of Anatomy and Image-Characteristic Features
• Authors: Sebastian Doerrich, Francesco Di Salvo, Christian Ledig
• Abstract summary: We introduce unORANIC, an unsupervised approach that uses an adapted loss function to drive the orthogonalization of anatomy and image-characteristic features. During test time unORANIC is applied to potentially corrupted images to reconstruct corruption-free images, showing their domain-invariant anatomy only. We confirm this qualitatively and quantitatively on 5 distinct datasets by assessing unORANIC's classification accuracy, corruption detection and revision capabilities. Our approach shows promise for enhancing the generalizability and robustness of practical applications in medical image analysis.
• Score: 0.14732811715354455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce unORANIC, an unsupervised approach that uses an adapted loss function to drive the orthogonalization of anatomy and image-characteristic features. The method is versatile for diverse modalities and tasks, as it does not require domain knowledge, paired data samples, or labels. During test time unORANIC is applied to potentially corrupted images, orthogonalizing their anatomy and characteristic components, to subsequently reconstruct corruption-free images, showing their domain-invariant anatomy only. This feature orthogonalization further improves generalization and robustness against corruptions. We confirm this qualitatively and quantitatively on 5 distinct datasets by assessing unORANIC's classification accuracy, corruption detection and revision capabilities. Our approach shows promise for enhancing the generalizability and robustness of practical applications in medical image analysis. The source code is available at https://github.com/sdoerrich97/unORANIC.

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