高效自监督Barlow Twins方法用于有限组织切片队列的结肠病理诊断
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology Diagnostics
AI总结：
本文提出优化的Barlow Twins自监督框架用于结直肠息肉筛查，调整超参数、增强策略和编码器，确定最佳视野，构建新基准数据集，并证明SSL表示优于监督表示且Swin Transformer有益。
摘要：
结直肠癌（CRC）是少数具有已确立的异型增生-癌序列的癌症之一，该序列可从筛查中获益。在加拿大，所有50岁以上的人都有资格接受CRC筛查。其中约20%的人将因癌前息肉而接受活检，且在许多情况下会存在多个息肉。因此，这些息肉活检构成了病理学家工作量的主体。开发一种高效的计算模型来帮助筛查这些息肉活检，可以改善病理学家的工作流程，并帮助将他们的注意力引导到切片上的关键区域。由于全切片图像具有十亿像素级的图像尺寸以及详细标注数据集的稀缺性，深度学习模型在计算病理学（CPath）中面临重大挑战。因此，利用自监督学习（SSL）方法来减轻数据标注的负担和成本至关重要。然而，当前研究缺乏将SSL框架有效应用于病理数据分析的方法。本文旨在提出一种针对结直肠息肉筛查优化的Barlow Twins框架。我们根据病理数据的特异性调整其超参数、增强策略和编码器以提升性能。此外，我们研究了结直肠息肉筛查的最佳视野（FoV），并通过在MHIST和NCT-CRC-7K数据集上进行下游任务，提出了一个新的CRC筛查基准数据集，该数据集由四种结直肠息肉和正常组织组成。进一步地，我们表明SSL表示比监督表示更有意义且更具定性优势，并且Barlow Twins在应用于病理数据时受益于Swin Transformer。代码可从https://github.com/AtlasAnalyticsLab/PathBT获取。
英文摘要：
Colorectal cancer (CRC) is one of the few cancers that have an established dysplasia-carcinoma sequence that benefits from screening. Everyone over 50 years of age in Canada is eligible for CRC screening. About 20\% of those people will undergo a biopsy for a pre-neoplastic polyp and, in many cases, multiple polyps. As such, these polyp biopsies make up the bulk of a pathologist's workload. Developing an efficient computational model to help screen these polyp biopsies can improve the pathologist's workflow and help guide their attention to critical areas on the slide. DL models face significant challenges in computational pathology (CPath) because of the gigapixel image size of whole-slide images and the scarcity of detailed annotated datasets. It is, therefore, crucial to leverage self-supervised learning (SSL) methods to alleviate the burden and cost of data annotation. However, current research lacks methods to apply SSL frameworks to analyze pathology data effectively. This paper aims to propose an optimized Barlow Twins framework for colorectal polyps screening. We adapt its hyperparameters, augmentation strategy and encoder to the specificity of the pathology data to enhance performance. Additionally, we investigate the best Field of View (FoV) for colorectal polyps screening and propose a new benchmark dataset for CRC screening, made of four types of colorectal polyps and normal tissue, by performing downstream tasking on MHIST and NCT-CRC-7K datasets. Furthermore, we show that the SSL representations are more meaningful and qualitative than the supervised ones and that Barlow Twins benefits from the Swin Transformer when applied to pathology data. Codes are avaialble from https://github.com/AtlasAnalyticsLab/PathBT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
