CytoSPM：基于结构化提示库的开放词汇细胞病理学检测

中文摘要 AI 辅助

细胞病理学检测需要开放词汇识别，因为细胞类别是细粒度的、长尾分布的，并且在不同器官系统中不断演变。然而，现有的细胞学检测器大多是单领域和封闭集的，目前仍缺乏评估开放词汇细胞病理学检测的统一基准。我们提出了PentaCyto，一个覆盖宫颈、泌尿、呼吸、浆液和甲状腺细胞学的多领域基准，包含24个基础类别和9个保留的新颖类别。每个类别都关联了结构化的细胞形态学提示，描述诊断性形态学属性并提供临床依据的文本知识。我们进一步提出了CytoSPM，一种基于解耦两阶段设计的高效检测器。它首先提取可复用的类别无关视觉表示，然后使用类别名称和细胞形态学提示进行类别感知的结构化提示匹配。在PentaCyto上，CytoSPM在新颖类别检测和开放词汇检测方面优于现有方法，同时保持高效推理。

英文摘要

Cytopathology detection requires open-vocabulary recognition because cellular categories are fine-grained, long-tailed, and continuously evolving across different organ systems. However, existing cytology detectors are mostly single-domain and closed-set, and there is still no unified benchmark for evaluating open-vocabulary cytopathology detection. We present PentaCyto, a multi-domain benchmark covering cervical, urinary, respiratory, serous fluid, and thyroid cytology, with 24 base categories and 9 held-out novel categories. Each category is associated with structured cytomorphology prompts that describe diagnostic morphological attributes and provide clinically grounded textual knowledge. We further propose CytoSPM, an efficient detector based on a decoupled two-stage design. It first extracts reusable class-agnostic visual representations, and then performs class-aware structural prompt matching with class names and cytomorphology prompts. On PentaCyto, CytoSPM outperforms existing methods in novel-category detection and open-vocabulary detection while maintaining efficient inference.

发表机构

• Central South University（中南大学）
• Shanghai Jiao Tong University（上海交通大学）

机构由 AI 辅助整理，请以论文原文为准。