Abstract
Digital pathology, augmented by artificial intelligence (AI), holds significant promise for improving the workflow of pathologists. However, challenges such as the labor-intensive annotation of whole slide images (WSIs), high computational demands, and trust concerns arising from the absence of uncertainty estimation in predictions hinder the practical application of current AI methodologies in histopathology. To address these issues, we present a novel trustful fully unsupervised multi-level segmentation methodology (TUMLS) for WSIs. TUMLS adopts an autoencoder (AE) as a feature extractor to identify the different tissue types within low-resolution training data. It selects representative patches from each identified group based on an uncertainty measure and then does unsupervised nuclei segmentation in their respective higher-resolution space without using any ML algorithms. Crucially, this solution integrates seamlessly into clinicians workflows, transforming the examination of a whole WSI into a review of concise, interpretable cross-level insights. This integration significantly enhances and accelerates the workflow while ensuring transparency. We evaluated our approach using the UPENN-GBM dataset, where the AE achieved a mean squared error (MSE) of 0.0016. Additionally, nucleus segmentation is assessed on the MoNuSeg dataset, outperforming all unsupervised approaches with an F1 score of 77.46% and a Jaccard score of 63.35%. These results demonstrate the efficacy of TUMLS in advancing the field of digital pathology.
Abstract (translated)
由人工智能（AI）增强的数字病理学在提高病理学家的工作流程方面具有巨大潜力。然而，诸如全滑数字图像（WSIs）人工标注劳动强度大、计算需求高以及由于缺乏预测不确定性估计而产生的信任问题等挑战阻碍了当前AI方法在组织病理学中的实际应用。为了解决这些问题，我们提出了一种新型的可信赖的完全无监督多层次分割方法（TUMLS），用于处理WSI。 TUMLS采用自动编码器（AE）作为特征提取器，利用低分辨率训练数据来识别不同类型的组织。该方法基于不确定性度量从每个已识别的组中选择代表性切片，并在相应的高分辨率空间内执行无监督细胞核分割，且不使用任何机器学习算法。这种方法的关键优势在于它可以无缝地集成到临床医生的工作流程中，将整个WSI的检查转变为对简洁、可解释的跨层次见解的审查。这种整合显著提升了工作流程的效率并确保了透明度。 我们利用UPENN-GBM数据集评估了我们的方法，其中AE在均方误差（MSE）指标上达到了0.0016的成绩。此外，在MoNuSeg数据集上的细胞核分割评估显示，TUMLS优于所有无监督方法，F1得分为77.46%，Jaccard得分则为63.35%。这些结果证明了TUMLS在推进数字病理学领域方面的有效性。
URL
https://arxiv.org/abs/2504.12718
