20.500.12556/RUL-181427 Patherea cell detection and classification for the 2020s We 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. classification detection Ki-67 vision transformer pathology klasifikacija zaznavanje vidni transformator patologija true false true Angleški jezik Slovenski jezik Članek v reviji 2026-04-07 10:57:45 2026-04-07 10:57:50 2026-06-05 03:02:00 0000-00-00 00:00:00 2026 0 0 17 str. art. 103868 Vol. 108 Feb. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 616 1361-8423 10.1016/j.media.2025.103868 265987331 1-s2.0-S1361841525004141-main.pdf 1-s2.0-S1361841525004141-main.pdf 1 F2A532E60D6177B46FAEC5A9261AD233 c6a6ed9d09ad13fde2daa15d513a875818b3dc318d2a99a993fa74e319c3e301 c30f1efc-5fe8-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=234940 https://www.sciencedirect.com/science/article/pii/S1361841525004141 0 acc44da4-325f-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=231853 Fakulteta za računalništvo in informatiko Medicinska fakulteta 0 0 0