论文标题
弱监督的多个实例学习组织病理学肿瘤细分
Weakly supervised multiple instance learning histopathological tumor segmentation
论文作者
论文摘要
在临床实践中，组织病理学图像分割是具有巨大潜在影响的医学成像中的一个具有挑战性且重要的主题。最先进的方法依赖于手工制作的注释，这阻碍了临床翻译，因为组织学遭受了癌症表型之间的显着差异。在本文中，我们提出了一个弱监督的框架，用于整个幻灯片成像细分，该框架依赖于大多数医疗系统中可用的标准临床注释。特别是，我们为培训模型开发了多个实例学习方案。该框架已在癌症基因组图集和PatchCamelyon数据集的多分解和多中心公共数据上进行了评估。与专家的注释相比，有希望的结果证明了提出的方法的潜力。完整的框架，包括$ 6481 $生成的肿瘤图和数据处理，可在https://github.com/marvinler/tcga_segmentation上获得。
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.
