AINeutralarXiv – CS AI · 9h ago6/10
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Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge
The MIDOG 2025 challenge evaluated automated mitosis detection across 365 diverse tumor cases spanning 12 different human, canine, and feline types to assess real-world clinical applicability. Results showed top F1 scores of 0.740 for detection and 0.908 balanced accuracy for atypical mitotic figure classification, but revealed significant performance degradation in challenging tissue areas where false positives tripled, highlighting major limitations in current AI architectures.