Patherea: Cell Detection and Classification for the 2020s
This work addresses the need for fair evaluation and standardized benchmarks in pathology image analysis, though it is incremental in improving existing methods.
The authors tackled the problem of cell detection and classification by introducing Patherea, a unified framework that achieves state-of-the-art performance on public datasets like Lizard, BRCA-M2C, and BCData, while also proposing a new, more challenging dataset for benchmarking.
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.