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

2025-06-23

AI Summary

This study addresses the challenge of jointly modeling multi-scale pathological images of malignant lymphoma—from nuclei to tissue—within a unified geometric space. Conventional Euclidean representations fail to capture the inherent hierarchical containment relationships among pathological structures. To overcome this, we propose a self-supervised joint embedding method grounded in the Poincaré ball model of hyperbolic space, which maps high-resolution nuclear and low-resolution tissue images into a shared hyperbolic latent space, explicitly encoding their intrinsic hierarchical semantic structure. The method operates without manual annotations, leveraging contrastive learning to align cross-scale features. Experiments demonstrate that the learned representations effectively discriminate disease stages and cell types, significantly improving both structural fidelity and discriminability of multi-scale morphological changes. Our work establishes a novel paradigm for hyperbolic representation learning in digital pathology, advancing geometric deep learning for hierarchical biomedical data.

Technology Category

• Machine Learning: Representation Learning
• Computer Vision: Representation Learning for Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 Poincaré ball as the feature space enables effective encoding of this hierarchical structure. The learned representations capture both disease state and cell type variations.

Problem

• Represent multi-scale lymphoma pathology images in hyperbolic space
• Capture morphological changes across scales during disease progression
• Encode hierarchical structure using Poincaré ball for disease and cell variations

Innovation

• Self-supervised learning in hyperbolic space
• Multi-scale embedding with inclusion relationships
• Poincaré ball for hierarchical feature encoding