Patherea : cell detection and classification for the 2020s

Štepec, Dejan (avtor), Jerše, Maja (avtor), Đokić, Snežana (avtor), Jeruc, Jera (avtor), Zidar, Nina (avtor), Skočaj, Danijel (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: classification, detection, Ki-67, vision transformer, pathology
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: MF - Medicinska fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: 17 str.
• Številčenje: Vol. 108, art. 103868
• PID: 20.500.12556/RUL-181427
• UDK: 616
• ISSN pri članku: 1361-8423
• DOI: 10.1016/j.media.2025.103868
• COBISS.SI-ID: 265987331
• Datum objave v RUL: 07.04.2026

Gradivo je del revije

• Naslov: Medical Image Analysis
• Založnik: Elsevier
• ISSN: 1361-8423
• COBISS.SI-ID: 23054085

Licence

• Licenca: CC BY-NC-ND 4.0, Creative Commons Priznanje avtorstva-Nekomercialno-Brez predelav 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by-nc-nd/4.0/deed.sl
• Opis: Najbolj omejujoča licenca Creative Commons. Uporabniki lahko prenesejo in delijo delo v nekomercialne namene in ga ne smejo uporabiti za nobene druge namene.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: klasifikacija, zaznavanje, vidni transformator, patologija

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P3-0054
• Naslov: Patologija in molekularna genetika

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P2-0214
• Naslov: Računalniški vid