循环学习：连接图像级标签与细胞核实例分割
Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation
AI总结：
针对细胞核实例分割像素级标注成本高的问题，提出图像级弱监督循环学习框架，通过分类与半监督分割循环共享知识生成并优化伪掩码，在三个数据集上优于同类弱监督方法并接近全监督性能。
摘要：
组织病理学图像上的细胞核实例分割对于疾病分析具有重要临床价值。通常，用于该任务的全监督算法需要像素级人工标注，而由于细胞核密度较高，这尤其耗时且费力。为减轻标注负担，我们寻求通过图像级弱监督学习来解决该问题，而这一方向在细胞核实例分割中尚未得到充分研究。与大多数现有方法使用其他弱标注（涂鸦、点等）进行细胞核实例分割相比，我们的方法更加节省人力。在细胞核实例分割中使用图像级标注的障碍在于缺乏足够的位置信息，从而导致严重的细胞核遗漏或重叠。本文提出一种名为循环学习的新型图像级弱监督方法来解决该问题。循环学习包含一个前端分类任务和一个后端半监督实例分割任务，以受益于多任务学习（MTL）。我们利用一个具有可解释性的深度学习分类器作为前端，将图像级标签转换为多组高置信度伪掩码，并建立一个半监督架构作为后端，在这些伪掩码的监督下执行细胞核实例分割。最重要的是，循环学习被设计为在前端分类器与后端半监督部分之间循环共享知识，使整个系统能够充分挖掘图像级标签中的潜在信息，并收敛到更优的最优点。在三个数据集上的实验证明了我们方法具有良好的泛化能力，其性能优于其他用于细胞核实例分割的图像级弱监督方法，并取得了与全监督方法相当的性能。
英文摘要：
Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis. Generally, fully-supervised algorithms for this task require pixel-wise manual annotations, which is especially time-consuming and laborious for the high nuclei density. To alleviate the annotation burden, we seek to solve the problem through image-level weakly supervised learning, which is underexplored for nuclei instance segmentation. Compared with most existing methods using other weak annotations (scribble, point, etc.) for nuclei instance segmentation, our method is more labor-saving. The obstacle to using image-level annotations in nuclei instance segmentation is the lack of adequate location information, leading to severe nuclei omission or overlaps. In this paper, we propose a novel image-level weakly supervised method, called cyclic learning, to solve this problem. Cyclic learning comprises a front-end classification task and a back-end semi-supervised instance segmentation task to benefit from multi-task learning (MTL). We utilize a deep learning classifier with interpretability as the front-end to convert image-level labels to sets of high-confidence pseudo masks and establish a semi-supervised architecture as the back-end to conduct nuclei instance segmentation under the supervision of these pseudo masks. Most importantly, cyclic learning is designed to circularly share knowledge between the front-end classifier and the back-end semi-supervised part, which allows the whole system to fully extract the underlying information from image-level labels and converge to a better optimum. Experiments on three datasets demonstrate the good generality of our method, which outperforms other image-level weakly supervised methods for nuclei instance segmentation, and achieves comparable performance to fully-supervised methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
