Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice. State of the art methods rely on hand-crafted annotations which hinder clinical translation since histology suffers from significant variations between cancer phenotypes. In this paper, we propose a weakly supervised framework for whole slide imaging segmentation that relies on standard clinical annotations, available in most medical systems. In particular, we exploit a multiple instance learning scheme for training models. The proposed framework has been evaluated on multi-locations and multi-centric public data from The Cancer Genome Atlas and the PatchCamelyon dataset. Promising results when compared with experts' annotations demonstrate the potentials of the presented approach. The complete framework, including $6481$ generated tumor maps and data processing, is available at https://github.com/marvinler/tcga_segmentation.
翻译:在医学成像中,病理学图象分解是一个具有挑战性和重要主题,对临床实践有巨大潜在影响。艺术状态的方法依靠手工制作的说明,妨碍临床翻译,因为生理学在癌症属类类型之间差异很大。在本文中,我们提议对整个幻灯片成像分解建立一个监督薄弱的框架,依靠大多数医疗系统提供的标准临床说明。我们特别利用一个培训模型的多实例学习计划。对拟议的框架进行了多功能和多中心公共数据评价,从癌症基因组图集和帕奇卡梅利翁数据集中获得了多功能和多中心公共数据。与专家说明相比,预期的结果显示了所提出方法的潜力。完整的框架,包括6481美元生成的肿瘤图和数据处理,可在https://github.com/marvinler/tcga_sementation查阅。