Contrastive learning has gained popularity due to its robustness with good feature representation performance. However, cosine distance, the commonly used similarity metric in contrastive learning, is not well suited to represent the distance between two data points, especially on a nonlinear feature manifold. Inspired by manifold learning, we propose a novel extension of contrastive learning that leverages geodesic distance between features as a similarity metric for histopathology whole slide image classification. To reduce the computational overhead in manifold learning, we propose geodesic-distance-based feature clustering for efficient contrastive loss evaluation using prototypes without time-consuming pairwise feature similarity comparison. The efficacy of the proposed method is evaluated on two real-world histopathology image datasets. Results demonstrate that our method outperforms state-of-the-art cosine-distance-based contrastive learning methods.

翻译：对比学习因其鲁棒性和良好的特征表示性能而受到广泛关注。然而，对比学习中常用的相似性度量——余弦距离，并不适合表示两个数据点之间的距离，尤其是在非线性特征流形上。受流形学习的启发，我们提出了一种新颖的对比学习扩展方法，利用特征间的测地距离作为相似性度量，用于组织病理学全切片图像分类。为降低流形学习的计算开销，我们提出了基于测地距离的特征聚类方法，通过原型实现高效的对比损失评估，无需耗时的成对特征相似性比较。所提方法在两个真实组织病理学图像数据集上进行了评估。结果表明，我们的方法优于基于余弦距离的最新对比学习方法。