Multi-Scale Representation of Follicular Lymphoma Pathology Images in a Single Hyperbolic Space

• URL: http://arxiv.org/abs/2506.18523v1
• Date: Mon, 23 Jun 2025 11:25:55 GMT
• Title: Multi-Scale Representation of Follicular Lymphoma Pathology Images in a Single Hyperbolic Space
• Authors: Kei Taguchi, Kazumasa Ohara, Tatsuya Yokota, Hiroaki Miyoshi, Noriaki Hashimoto, Ichiro Takeuchi, Hidekata Hontani,
• Abstract summary: We propose a method for representing malignant lymphoma pathology images using self-supervised learning.<n>To capture morphological changes that occur across scales during disease progression, our approach embeds tissue and corresponding nucleus images close to each other.
• Score: 16.779755785147195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a method for representing malignant lymphoma pathology images, from high-resolution cell nuclei to low-resolution tissue images, within a single hyperbolic space using self-supervised learning. To capture morphological changes that occur across scales during disease progression, our approach embeds tissue and corresponding nucleus images close to each other based on inclusion relationships. Using the Poincar\'e ball as the feature space enables effective encoding of this hierarchical structure. The learned representations capture both disease state and cell type variations.

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