Automatic nuclei segmentation and classification play a vital role in digital pathology. However, previous works are mostly built on data with limited diversity and small sizes, making the results questionable or misleading in actual downstream tasks. In this paper, we aim to build a reliable and robust method capable of dealing with data from the 'the clinical wild'. Specifically, we study and design a new method to simultaneously detect, segment, and classify nuclei from Haematoxylin and Eosin (H&E) stained histopathology data, and evaluate our approach using the recent largest dataset: PanNuke. We address the detection and classification of each nuclei as a novel semantic keypoint estimation problem to determine the center point of each nuclei. Next, the corresponding class-agnostic masks for nuclei center points are obtained using dynamic instance segmentation. Meanwhile, we proposed a novel Joint Pyramid Fusion Module (JPFM) to model the cross-scale dependencies, thus enhancing the local feature for better nuclei detection and classification. By decoupling two simultaneous challenging tasks and taking advantage of JPFM, our method can benefit from class-aware detection and class-agnostic segmentation, thus leading to a significant performance boost. We demonstrate the superior performance of our proposed approach for nuclei segmentation and classification across 19 different tissue types, delivering new benchmark results.

翻译：自动细胞核分割与分类在数字病理学中发挥着关键作用。然而，以往的研究大多基于多样性有限且规模较小的数据集，导致其结果在实际下游任务中可能存在问题或产生误导。本文旨在构建一种可靠且稳健的方法，以处理来自"临床野生环境"的数据。具体而言，我们研究并设计了一种新方法，能够同时检测、分割和分类苏木精-伊红（H&E）染色组织病理学数据中的细胞核，并使用当前最大数据集PanNuke评估我们方法的性能。我们将每个细胞核的检测与分类视为一种新颖的语义关键点估计问题，以确定每个细胞核的中心点。随后，利用动态实例分割获取对应细胞核中心点的类别无关掩膜。同时，我们提出了一种新颖的联合金字塔融合模块（JPFM），用于建模跨尺度依赖关系，从而增强局部特征以改进细胞核检测与分类。通过解耦两个同步的挑战性任务并利用JPFM的优势，我们的方法能够从类别感知检测和类别无关分割中获益，从而显著提升性能。我们证明了所提方法在19种不同组织类型上对细胞核分割与分类的优越性能，并提供了新的基准结果。