Renal Cell Carcinoma is typically asymptomatic at the early stages for many patients. This leads to a late diagnosis of the tumor, where the curability likelihood is lower, and makes the mortality rate of Renal Cell Carcinoma high, with respect to its incidence rate. To increase the survival chance, a fast and correct categorization of the tumor subtype is paramount. Nowadays, computerized methods, based on artificial intelligence, represent an interesting opportunity to improve the productivity and the objectivity of the microscopy-based Renal Cell Carcinoma diagnosis. Nonetheless, much of their exploitation is hampered by the paucity of annotated dataset, essential for a proficient training of supervised machine learning technologies. This study sets out to investigate a novel self supervised training strategy for machine learning diagnostic tools, based on the multi-resolution nature of the histological samples. We aim at reducing the need of annotated dataset, without significantly reducing the accuracy of the tool. We demonstrate the classification capability of our tool on a whole slide imaging dataset for Renal Cancer subtyping, and we compare our solution with several state-of-the-art classification counterparts.

翻译：肾细胞癌在早期阶段通常对许多患者无症状。这导致肿瘤诊断较晚，此时治愈可能性较低，并使肾细胞癌的死亡率相对于其发病率较高。为提高生存机会，快速准确地对肿瘤亚型进行分类至关重要。如今，基于人工智能的计算机化方法为提高基于显微镜的肾细胞癌诊断的生产力和客观性提供了重要机遇。然而，由于缺乏标注数据集（这对于监督机器学习技术的熟练训练至关重要），其应用在很大程度上受到限制。本研究旨在探索一种基于组织学样本多分辨率特性的新型自监督训练策略，用于机器学习诊断工具。我们的目标是在不显著降低工具准确性的前提下减少对标注数据集的需求。我们在用于肾癌亚型分类的全切片成像数据集上验证了工具的分类能力，并将我们的解决方案与多种先进的分类方法进行了比较。