统一框架实现细胞检测与分类，推动病理图像分析标准化
Patherea: Cell Detection and Classification for the 2020s
- 基于点的直接预测，无需中间表示，提升检测效率
- 在三大公开数据集上达顶尖性能，新数据集挑战更大
- 修正评估漏洞，提供可复现的基准工具，适合临床研究者
我们提出 Patherea，一个统一的基于点的细胞检测与分类框架，支持先进方法的开发与公平评估。为此，我们构建了一个大规模数据集，复现了Ki-67增殖指数估算的临床工作流程。该方法直接预测细胞位置与类别，不依赖中间表示，采用混合匈牙利匹配策略实现精准点分配，并支持灵活的骨干网络与训练方式，包括最新的病理基础模型。Patherea在公共数据集Lizard、BRCA-M2C和BCData上达到当前最优性能，同时揭示这些基准已接近性能饱和。相比之下，我们提出的Patherea数据集构成更具挑战性的新基准。此外，我们识别并纠正了现有评估协议中的常见错误，提供了更新的基准评估工具以实现标准化测试。Patherea数据集与代码已开源，促进后续研究与公平比较。
原文摘要 · Abstract (English)
We present Patherea, a unified framework for point-based cell detection and classification that enables the development and fair evaluation of state-of-the-art methods. To support this, we introduce a large-scale dataset that replicates the clinical workflow for Ki-67 proliferation index estimation. Our method directly predicts cell locations and classes without relying on intermediate representations. It incorporates a hybrid Hungarian matching strategy for accurate point assignment and supports flexible backbones and training regimes, including recent pathology foundation models. Patherea achieves state-of-the-art performance on public datasets - Lizard, BRCA-M2C, and BCData - while highlighting performance saturation on these benchmarks. In contrast, our newly proposed Patherea dataset presents a significantly more challenging benchmark. Additionally, we identify and correct common errors in current evaluation protocols and provide an updated benchmarking utility for standardized assessment. The Patherea dataset and code are publicly available to facilitate further research and fair comparisons.
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