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AI models struggle with real-world mitosis detection in pathology challenge

By PulseAugur Editorial · [2 sources] ·

The MIDOG 2025 challenge evaluated AI models for detecting mitosis across diverse biological and contextual scenarios, moving beyond traditional hotspot analysis. The challenge included detecting atypical mitotic figures and tested models on 12 different tumor types across various scanning platforms. Results showed significant performance drops in challenging regions and across different tumor types, indicating current models struggle with real-world clinical variability. AI

IMPACT Highlights the need for more robust AI in pathology to handle real-world data variability, potentially improving diagnostic accuracy.

RANK_REASON The cluster contains an academic paper detailing a challenge and its results.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI models struggle with real-world mitosis detection in pathology challenge

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Marc Aubreville, Jonas Ammeling, Sweta Banerjee, Viktoria Weiss, Taryn A. Donovan, Robert Klopfleisch, Jiaqi Lv, Shan E Ahmed Raza, Rapha\"el Bourgade, Thomas Walter, Yasemin Topuz, Song\"ul Varl{\i}, Charles-Antoine Collins-Fekete, Zhuoyan Shen, Navya S… ·

    Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge

    arXiv:2606.07368v1 Announce Type: cross Abstract: Automated mitosis detection is a well-established task in computational pathology. While previous benchmarks focused on scanner-induced domain shift, clinical "real-world" application requires models to be robust across the vast v…

  2. arXiv cs.CV TIER_1 English(EN) · Christof A. Bertram ·

    Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge

    Automated mitosis detection is a well-established task in computational pathology. While previous benchmarks focused on scanner-induced domain shift, clinical "real-world" application requires models to be robust across the vast variance to be expected in the histological landsca…

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