PointNu-Net：用于同时进行多组织组织学细胞核分割与分类的关键点辅助卷积神经网络
PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification
AI总结：
本文提出 PointNu-Net，将细胞核检测与分类建模为语义关键点估计，结合动态实例分割和联合金字塔融合模块 JPFM，在 PanNuke 数据集上实现了 19 种组织类型的细胞核分割与分类新基准。
摘要：
自动细胞核分割与分类在数字病理学中起着至关重要的作用。然而，以往的工作大多建立在多样性有限且规模较小的数据上，使得其结果在实际下游任务中值得怀疑或具有误导性。在本文中，我们旨在构建一种可靠且鲁棒的方法，能够处理来自“临床真实环境”的数据。具体而言，我们研究并设计了一种新方法，用于从苏木精-伊红（H&E）染色的组织病理学数据中同时检测、分割和分类细胞核，并使用近期最大的数据集 PanNuke 评估我们的方法。我们将每个细胞核的检测与分类作为一个新颖的语义关键点估计问题来处理，以确定每个细胞核的中心点。接下来，使用动态实例分割获得细胞核中心点对应的类别无关掩码。同时，我们提出了一种新颖的联合金字塔融合模块（JPFM）来建模跨尺度依赖关系，从而增强局部特征以更好地进行细胞核检测与分类。通过解耦两个同时具有挑战性的任务并利用 JPFM，我们的方法能够受益于类别感知检测和类别无关分割，从而带来显著的性能提升。我们证明了所提出的方法在 19 种不同组织类型上的细胞核分割与分类具有优越性能，并给出了新的基准结果。
英文摘要：
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.
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。
