自动生成精准定位与分类提示，提升病理核分割与分类精度
APSeg: Auto-Prompt Model with Acquired and Injected Knowledge for Nuclear Instance Segmentation and Classification
- 通过密度图引导学习分布知识，生成更准的候选框
- 注入形态学类别知识，提升分类准确性，达85.6%平均F1
- 适合医学图像分析、数字病理诊断研究者使用
核实例分割与分类为数字病理诊断提供了关键定量基础。随着基础分割模型SAM的出现，核分割的准确性和效率显著提升。然而，SAM对精确提示依赖性强，且其无类别设计使分类结果完全取决于提示内容。为此，我们提出APSeg——一种结合获取与注入知识的自动提示模型，用于核实例分割与分类。APSeg包含两个知识感知模块：(1) 分布引导提示偏移模块（DG-POM），通过密度图引导学习分布知识；(2) 类别知识语义注入模块（CK-SIM），注入基于类别描述提取的形态学知识。我们在PanNuke和CoNSeP数据集上进行了大量实验，验证了该方法的有效性。代码将在录用后公开。
原文摘要 · Abstract (English)
Nuclear instance segmentation and classification provide critical quantitative foundations for digital pathology diagnosis. With the advent of the foundational Segment Anything Model (SAM), the accuracy and efficiency of nuclear segmentation have improved significantly. However, SAM imposes a strong reliance on precise prompts, and its class-agnostic design renders its classification results entirely dependent on the provided prompts. Therefore, we focus on generating prompts with more accurate localization and classification and propose \textbf{APSeg}, \textbf{A}uto-\textbf{P}rompt model with acquired and injected knowledge for nuclear instance \textbf{Seg}mentation and classification. APSeg incorporates two knowledge-aware modules: (1) Distribution-Guided Proposal Offset Module (\textbf{DG-POM}), which learns distribution knowledge through density map guided, and (2) Category Knowledge Semantic Injection Module (\textbf{CK-SIM}), which injects morphological knowledge derived from category descriptions. We conducted extensive experiments on the PanNuke and CoNSeP datasets, demonstrating the effectiveness of our approach. The code will be released upon acceptance.
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