Recently, deep learning methods have been successfully applied to solve numerous challenges in the field of digital pathology. However, many of these approaches are fully supervised and require annotated images. Annotating a histology image is a time-consuming and tedious process for even a highly skilled pathologist, and, as such, most histology datasets lack region-of-interest annotations and are weakly labeled. In this paper, we introduce HistoPerm, a view generation approach designed for improving the performance of representation learning techniques on histology images in weakly supervised settings. In HistoPerm, we permute augmented views of patches generated from whole-slide histology images to improve classification accuracy. These permuted views belong to the same original slide-level class but are produced from distinct patch instances. We tested adding HistoPerm to BYOL and SimCLR, two prominent representation learning methods, on two public histology datasets for Celiac disease and Renal Cell Carcinoma. For both datasets, we found improved performance in terms of accuracy, F1-score, and AUC compared to the standard BYOL and SimCLR approaches. Particularly, in a linear evaluation configuration, HistoPerm increases classification accuracy on the Celiac disease dataset by 8% for BYOL and 3% for SimCLR. Similarly, with HistoPerm, classification accuracy increases by 2% for BYOL and 0.25% for SimCLR on the Renal Cell Carcinoma dataset. The proposed permutation-based view generation approach can be adopted in common representation learning frameworks to capture histopathology features in weakly supervised settings and can lead to whole-slide classification outcomes that are close to, or even better than, fully supervised methods.
翻译:最近,在数字病理学领域,已经成功地应用了深层次学习方法来解决众多挑战。然而,许多这些方法都受到充分监督,需要附加说明的图像。对高技能病理学家来说,直观图像是一个耗时和乏味的过程,因此,大多数神学数据集缺乏区域利益说明,而且标签不高。在本文中,我们引入了历史图Perm,这是一种旨在改进在受监管薄弱的环境下对组织图像学图象学教学技术的绩效的视觉生成方法。在HistoPerm中,我们检查了从全光化组织图象图象图象图像中产生的补丁的强化观点,以提高分类的准确性。这些透析观点属于相同的原始幻灯片级,但也是从不同的补补码中生成的。我们测试了BYOL和SimCLLL,两种突出的代言法学习方法,在两个公共直径氏病理学数据库和Renal Cell Cell Chocroma中,我们发现在精度、F1-LO-ILO 和S-LE-LE-LE-LA中,在SD-LE-LA中,通过S-LI-I-I-I-L-S-I-I-L-I-LV-L-L-L-L-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-L-S-L-L-L-L-L-L-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-L-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-I-