PathereaŠtepec, Dejan (Avtor) Jerše, Maja (Avtor) Đokić, Snežana (Avtor) Jeruc, Jera (Avtor) Zidar, Nina (Avtor) Skočaj, Danijel (Avtor) classificationdetectionKi-67vision transformerpathologyWe present Patherea, a unified framework for point-based cell detection and classification in histopathology. Our method directly predicts cell locations and classes without intermediate representations and incorporates a hybrid Hungarian matching strategy for more accurate point assignment. Patherea supports flexible backbones and training regimes and leverages pathology foundation models for point-based detection, a capability not feasible with most existing point-based methods. To support method development and benchmarking, we introduce the largest fully manually labeled dataset for Ki-67 IHC, annotated by expert pathologists on full-resolution whole-slide images following standard clinical protocols. We evaluate our approach on existing public point-based datasets–Lizard, BRCA-M2C, and BCData–demonstrating superior F1 scores compared to recent point-based detection methods, while revealing performance saturation on these benchmarks. The Patherea dataset provides a complementary benchmark, capturing clinically relevant low-abundance cell classes where current approaches underperform. Finally, we identify and correct common errors in existing evaluation protocols and provide a benchmarking utility for reproducible assessment. The Patherea dataset and code are publicly released to support future research and fair comparisons.20262026-04-07 10:57:45Članek v reviji181427sl