细胞核实例分割与分类的调研：利用上下文与注意力机制

核源性形态特征和生物标志物为肿瘤微环境提供相关见解，同时也有助于特定癌症类型的诊断和预后。然而，从基因组全切片的Hematoxylin和Eosin（H&E）染色的全切片图像（WSI）中手动标注细胞核是一项繁重且昂贵的任务，因此自动化的细胞核实例分割与分类算法可以减轻病理学家和临床研究者的工作负担，同时促进临床可解释特征的自动提取，用于人工智能（AI）工具。然而，由于细胞核形态和染色特征的高度内在和类别间变异，以及H&E染色易受伪影影响，现有最先进的算法难以以必要的性能正确检测和分类实例。在本研究中，我们假设人工神经网络（ANNs）中的上下文和注意力归纳偏差能够提高细胞核实例分割与分类的性能和泛化能力。为了理解上下文和注意力机制在实例分割与分类中的优势、应用场景及局限性，我们首先回顾了计算机视觉和医学影像领域的相关工作。随后，系统调研了基于上下文和注意力机制的细胞核实例分割与分类方法，讨论了这些机制所解决的主要挑战。此外，我们还指出了当前方法的局限性，并提出了未来研究的思路。作为案例研究，我们在一组多中心结肠核识别与计数数据集上，结合上下文和注意力机制，扩展了通用（Mask-RCNN）和定制（HoVer-Net）实例分割与分类方法，并进行了对比分析。尽管病理学家在分析和标注WSI时会在多个层面依赖上下文，并关注特定兴趣区域（RoIs），但我们的研究结果表明，将这一领域知识转化为算法设计并非易事，为充分发挥这些机制在ANN中的作用，首先需要对这些方法的科学基础进行深入理解。

Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs) is a laborious and costly task, meaning automated algorithms for cell nuclei instance segmentation and classification could alleviate the workload of pathologists and clinical researchers and at the same time facilitate the automatic extraction of clinically interpretable features for artificial intelligence (AI) tools. But due to high intra- and inter-class variability of nuclei morphological and chromatic features, as well as H&E-stains susceptibility to artefacts, state-of-the-art algorithms cannot correctly detect and classify instances with the necessary performance. In this work, we hypothesize context and attention inductive biases in artificial neural networks (ANNs) could increase the performance and generalization of algorithms for cell nuclei instance segmentation and classification. To understand the advantages, use-cases, and limitations of context and attention-based mechanisms in instance segmentation and classification, we start by reviewing works in computer vision and medical imaging. We then conduct a thorough survey on context and attention methods for cell nuclei instance segmentation and classification from H&E-stained microscopy imaging, while providing a comprehensive discussion of the challenges being tackled with context and attention. Besides, we illustrate some limitations of current approaches and present ideas for future research. As a case study, we extend both a general (Mask-RCNN) and a customized (HoVer-Net) instance segmentation and classification methods with context- and attention-based mechanisms and perform a comparative analysis on a multicentre dataset for colon nuclei identification and counting. Although pathologists rely on context at multiple levels while paying attention to specific Regions of Interest (RoIs) when analysing and annotating WSIs, our findings suggest translating that domain knowledge into algorithm design is no trivial task, but to fully exploit these mechanisms in ANNs, the scientific understanding of these methods should first be addressed.