Cancer detection and classification from gigapixel whole slide images of stained tissue specimens has recently experienced enormous progress in computational histopathology. The limitation of available pixel-wise annotated scans shifted the focus from tumor localization to global slide-level classification on the basis of (weakly-supervised) multiple-instance learning despite the clinical importance of local cancer detection. However, the worse performance of these techniques in comparison to fully supervised methods has limited their usage until now for diagnostic interventions in domains of life-threatening diseases such as cancer. In this work, we put the focus back on tumor localization in form of a patch-level classification task and take up the setting of so-called coarse annotations, which provide greater training supervision while remaining feasible from a clinical standpoint. To this end, we present a novel ensemble method that not only significantly improves the detection accuracy of metastasis on the open CAMELYON16 data set of sentinel lymph nodes of breast cancer patients, but also considerably increases its robustness against noise while training on coarse annotations. Our experiments show that better results can be achieved with our technique making it clinically feasible to use for cancer diagnosis and opening a new avenue for translational and clinical research.

翻译：从染色组织标本的千兆像素全切片图像中进行癌症检测与分类，近期在计算组织病理学领域取得了巨大进展。尽管局部癌症检测具有临床重要性，但由于像素级标注扫描的局限性，研究重点已从肿瘤定位转向基于（弱监督）多实例学习的全局切片级分类。然而，与全监督方法相比，这些技术性能较差，限制了其在癌症等致命疾病诊断干预中的应用。本研究将焦点重新置于以补丁级分类任务形式实现的肿瘤定位，并采用所谓的粗标注设置——该设置在保持临床可行性的同时提供了更强的训练监督。为此，我们提出了一种新型集成方法，不仅显著提升了公开的CAMELYON16数据集（乳腺癌患者前哨淋巴结）中转移灶的检测精度，还在基于粗标注训练时大幅增强了模型对抗噪声的鲁棒性。实验表明，我们的技术可获得更优结果，使其具备临床癌症诊断的可行性，并为转化医学与临床研究开辟了新途径。