This paper proposes a dynamic interactive and weakly supervised segmentation
method with minimal user interactions to address two major challenges in the
segmentation of whole slide histopathology images. First, the lack of
hand-annotated datasets to train algorithms. Second, the lack of interactive
paradigms to enable a dialogue between the pathologist and the machine, which
can be a major obstacle for use in clinical routine.
We therefore propose a fast and user oriented method to bridge this gap by
giving the pathologist control over the final result while limiting the number
of interactions needed to achieve a good result (over 90\% on all our metrics
with only 4 correction scribbles).

本文提出了一种动态交互和弱监督分割方法，以最小用户交互方式解决全幻灯组织病理图像分割中的两个主要挑战：缺乏手工标注的数据集来训练算法以及缺乏能够对话的交互模式，这在临床实践中可能是一个主要障碍。因此，我们提出了一种快速和用户导向的方法来弥合这一差距，让病理学家对最终结果有控制权的同时，限制所需交互的数量以获得良好的结果（仅需 4 次校正示意图即可在所有度量标准上实现 90％以上）。

基于涂鸦的快速弱监督分割全幅切片图像

Scribble-based fast weak-supervision and interactive corrections for  segmenting whole slide images

Processing giga-pixel whole slide histopathology images (WSI) is a
computationally expensive task. Multiple instance learning (MIL) has become the
conventional approach to process WSIs, in which these images are split into
smaller patches for further processing. However, MIL-based techniques ignore
explicit information about the individual cells within a patch. In this paper,
by defining the novel concept of shared-context processing, we designed a
multi-modal Graph Transformer (AMIGO) that uses the celluar graph within the
tissue to provide a single representation for a patient while taking advantage
of the hierarchical structure of the tissue, enabling a dynamic focus between
cell-level and tissue-level information. We benchmarked the performance of our
model against multiple state-of-the-art methods in survival prediction and
showed that ours can significantly outperform all of them including
hierarchical Vision Transformer (ViT). More importantly, we show that our model
is strongly robust to missing information to an extent that it can achieve the
same performance with as low as 20% of the data. Finally, in two different
cancer datasets, we demonstrated that our model was able to stratify the
patients into low-risk and high-risk groups while other state-of-the-art
methods failed to achieve this goal. We also publish a large dataset of
immunohistochemistry images (InUIT) containing 1,600 tissue microarray (TMA)
cores from 188 patients along with their survival information, making it one of
the largest publicly available datasets in this context.

通过定义共享环境处理的新概念，设计了一种多模式图变换器（AMIGO），该变换器利用细胞图来提供患者的单一表示，并利用组织的分层结构，实现细胞级和组织级信息之间的动态关注，从而在生存预测方面显着优于包括 ViT 在内的所有其他现有技术，并且强大的鲁棒性表现使其可以在缺失 20％的数据的情况下实现相同的性能，最后，在两个不同的癌症数据集中，演示了我们的模型能够将患者分为低风险和高风险组。