保持准确与稳健：一种增强的细胞核分析框架
Keep It Accurate and Robust: An Enhanced Nuclei Analysis Framework
AI总结：
针对组织学图像中细胞核分割与分类的挑战，提出双层集成建模及增强的HoVer-Net框架，在CoNIC 2022挑战中取得领先成绩，并开源代码与模型。
摘要：
在组织学图像中对细胞核进行准确的分割和分类是至关重要的，但由于细胞核的异质性、染色变化和组织复杂性，这一任务具有挑战性。现有方法往往受限于数据集多样性不足，因为从相似的全切片图像（WSI）中提取的补丁使得模型容易陷入局部最优。在此，我们提出一个新框架来解决这一局限，并实现稳健的细胞核分析。我们的方法利用双层集成建模来克服由有限数据集变化引起的问题。内部集成对单个样本应用多种变换，而外部集成则结合不同尺度的网络。我们还对HoVer-Net架构进行了增强，包括更新的编码器、嵌套密集解码和模型正则化策略。我们在公共基准上取得了最先进的结果，包括在2022年结肠细胞核识别与计数（CoNIC）挑战中，核组成预测获得第一名，分割/分类获得第三名。这一成功验证了我们的方法在准确组织学细胞核分析中的有效性。广泛的实验和消融研究为最优网络设计选择与训练技术提供了见解。总之，本工作提出了一个改进的框架，推动了细胞核分析的最先进水平。我们发布了代码和模型（https://github.com/WinnieLaugh/CONIC_Pathology_AI），作为社区的实用工具包。
英文摘要：
Accurate segmentation and classification of nuclei in histology images is critical but challenging due to nuclei heterogeneity, staining variations, and tissue complexity. Existing methods often struggle with limited dataset variability, with patches extracted from similar whole slide images (WSI), making models prone to falling into local optima. Here we propose a new framework to address this limitation and enable robust nuclear analysis. Our method leverages dual-level ensemble modeling to overcome issues stemming from limited dataset variation. Intra-ensembling applies diverse transformations to individual samples, while inter-ensembling combines networks of different scales. We also introduce enhancements to the HoVer-Net architecture, including updated encoders, nested dense decoding and model regularization strategy. We achieve state-of-the-art results on public benchmarks, including 1st place for nuclear composition prediction and 3rd place for segmentation/classification in the 2022 Colon Nuclei Identification and Counting (CoNIC) Challenge. This success validates our approach for accurate histological nuclei analysis. Extensive experiments and ablation studies provide insights into optimal network design choices and training techniques. In conclusion, this work proposes an improved framework advancing the state-of-the-art in nuclei analysis. We release our code and models (https://github.com/WinnieLaugh/CONIC_Pathology_AI) to serve as a toolkit for the community.
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。
