用于组织病理学鲁棒无监督分割的等变成像生物标志物
Equivariant Imaging Biomarkers for Robust Unsupervised Segmentation of Histopathology
AI总结：
本文针对组织病理图像中ML模型缺乏旋转不变性的问题，提出通过无监督分割和对称卷积核提取等变生物标志物，并在Gleason 2019 Challenge数据集上验证其鲁棒性和泛化能力。
摘要：
通过显微镜检查对组织标本进行组织病理学评估对于准确的疾病诊断和预后至关重要。然而，由经过专门训练的病理学家进行的传统人工分析耗时、劳动密集、成本效益低，并且容易出现评估者间的差异，可能影响诊断的一致性和准确性。随着数字病理图像的持续激增，迫切需要自动化分析来应对这些挑战。基于人工智能的工具（如机器学习（ML）模型）的最新进展显著提高了分析组织病理学切片的精度和效率。然而，尽管其性能令人印象深刻，ML模型仅对平移具有不变性，缺乏对旋转和反射的不变性。这一局限性限制了它们有效泛化的能力，尤其是在图像本质上缺乏有意义方向的组织病理学中。在本研究中，我们通过无监督分割，利用一种新颖的对称卷积核开发了鲁棒的等变组织病理学生物标志物。该方法使用来自Gleason 2019 Challenge公共数据集中50名患者的前列腺组织微阵列（TMA）图像进行了验证。与使用标准卷积核的模型相比，通过该方法提取的生物标志物在旋转情况下表现出增强的鲁棒性和泛化能力，有望提高数字病理学中ML模型的准确性、一致性和鲁棒性。最终，这项工作旨在通过等变成像提升组织病理学在前列腺癌之外的诊断和预后能力。
英文摘要：
Histopathology evaluation of tissue specimens through microscopic examination is essential for accurate disease diagnosis and prognosis. However, traditional manual analysis by specially trained pathologists is time-consuming, labor-intensive, cost-inefficient, and prone to inter-rater variability, potentially affecting diagnostic consistency and accuracy. As digital pathology images continue to proliferate, there is a pressing need for automated analysis to address these challenges. Recent advancements in artificial intelligence-based tools such as machine learning (ML) models, have significantly enhanced the precision and efficiency of analyzing histopathological slides. However, despite their impressive performance, ML models are invariant only to translation, lacking invariance to rotation and reflection. This limitation restricts their ability to generalize effectively, particularly in histopathology, where images intrinsically lack meaningful orientation. In this study, we develop robust, equivariant histopathological biomarkers through a novel symmetric convolutional kernel via unsupervised segmentation. The approach is validated using prostate tissue micro-array (TMA) images from 50 patients in the Gleason 2019 Challenge public dataset. The biomarkers extracted through this approach demonstrate enhanced robustness and generalizability against rotation compared to models using standard convolution kernels, holding promise for enhancing the accuracy, consistency, and robustness of ML models in digital pathology. Ultimately, this work aims to improve diagnostic and prognostic capabilities of histopathology beyond prostate cancer through equivariant imaging.
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。
