用双曲空间统一表示淋巴瘤病理图像的多尺度特征
Multi-Scale Representation of Follicular Lymphoma Pathology Images in a Single Hyperbolic Space
- 基于包含关系将细胞核与组织图像映射到同一双曲空间
- 学习到的表示能同时捕捉疾病状态和细胞类型差异
- 适合病理图像分析与医学影像智能诊断研究者
我们提出一种方法，将恶性淋巴瘤病理图像从高分辨率细胞核到低分辨率组织图像，在单一双曲空间中进行表示，采用自监督学习。为捕捉疾病进展过程中跨尺度的形态变化，该方法依据包含关系将组织图像与对应的细胞核图像嵌入相近位置。使用庞加莱球作为特征空间，有效编码了这种分层结构。所学表示能够捕捉疾病状态和细胞类型的变化。
原文摘要 · Abstract (English)
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.
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