无标签核心集选择：基于基础模型的计算病理学高效标注

中文摘要 AI 辅助

计算病理学通过已证实的诊断和预后准确性提升，有望改善临床结果。然而，深度学习算法的开发和验证仍需标注数据，这是一个昂贵的过程，需要面临严重人力短缺的病理学专家参与。现有的核心集选择方法用于优化标注工作，但均依赖于在自然图像基准上调整的超参数，这些参数无法迁移到组织病理学，且在临床实践中使用繁琐。在本研究中，我们提出GCcore，一种新颖的无标签核心集选择方法，该方法使用任何病理学基础模型嵌入数据集中的每张图像，并贪婪地选择能够共同最大化嵌入空间全局覆盖的样本。所提方法为任何核心集大小下返回核心集的全局覆盖提供了下界保证，同时完全无需超参数且具有确定性。我们展示了GCcore在14个基线方法（包括最先进方法）中的优越性能，涵盖10个任务和数据集，包括全切片图像分类、图像块分类和组织分割，在其中6个任务中排名第一，9个任务中进入前三，同时展示了现有方法如何根据其超参数设置而排名最多移动五个位置。代码可在以下网址公开获取：https URL。

英文摘要

Computational pathology has the potential to improve clinical outcomes through a demonstrated increase in diagnostic and prognostic accuracy. However, the development and validation of deep learning algorithms still require annotated data, a costly procedure involving expert pathologists who already face critical workforce shortages. Existing coreset selection methods to optimize annotation efforts currently all rely on hyperparameters tuned on natural-image benchmarks that do not transfer to histopathology and are cumbersome to use in clinical practice. In this study, we present GCcore, a novel label-free coreset selection method that embeds every image of a dataset with any pathology foundation model and greedily selects the samples that collectively maximize the global coverage of the embedding space. The proposed method provides a lower-bound guarantee on the global coverage of the returned coreset for any coreset size, while being completely hyperparameter-free and deterministic. We demonstrate GCcore's superior performance over 14 baselines including state-of-the-art methods across 10 tasks and datasets spanning whole slide image classification, tile classification, and tissue segmentation, where it ranks first on six and within the top three on nine, while also demonstrating how existing methods can shift by up to five rank positions depending on their hyperparameter settings. Code is publicly available at https://github.com/OncoAI-ULBHUB/GCcore.

发表机构

• Institut Jules Bordet（朱尔·博尔代研究所）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

32 pages, 7 figures