Abstract
Self-supervised learning (SSL) has emerged as a key technique for training networks that can generalize well to diverse tasks without task-specific supervision. This property makes SSL desirable for computational pathology, the study of digitized images of tissues, as there are many target applications and often limited labeled training samples. However, SSL algorithms and models have been primarily developed in the field of natural images and whether their performance can be improved by adaptation to particular domains remains an open question. In this work, we present an investigation of modifications to SSL for pathology data, specifically focusing on the DINOv2 algorithm. We propose alternative augmentations, regularization functions, and position encodings motivated by the characteristics of pathology images. We evaluate the impact of these changes on several benchmarks to demonstrate the value of tailored approaches.
Abstract (translated)
自监督学习（SSL）作为一种能够在没有任务特定监督的情况下泛化良好的网络训练技术，成为了一个关键的技术，尤其是在计算病理学中，病理学研究的数字图像。这种特性使得 SSL 成为计算病理学研究的理想选择，因为该领域有许多目标应用，但通常缺乏足够的标记训练样本。然而， SSL 算法和模型主要在自然图像领域开发，其性能是否可以通过适应特定领域来提高仍然是一个未解决的问题。在这项工作中，我们研究了针对病理数据的对 SSL 的修改，特别关注 DINOv2 算法。我们提出了由病理图像特征启发的替代增强、正则化函数和位置编码。我们评估了这些变化对多个基准测试的影响，以展示定制方法的价值。
URL
https://arxiv.org/abs/2405.01688
