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 this https URL

本文提出了一种弱监督整个切片图像分割的框架，利用标准的临床注释，采用多实例学习方案进行模型训练，并在 The Cancer Genome Atlas 和 PatchCamelyon 数据集上进行了评估，显示出一定的潜力.