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 variance to be expected in the histological landscape. The MItosis DOmain Generalization (MIDOG) 2025 challenge was designed to evaluate algorithmic performance across unprecedented biological and contextual diversity. We curated a test dataset of 365 cases, encompassing 12 distinct human, canine and feline tumor types,
The proliferation of digital pathology and advancements in AI inference capabilities are creating demand for robust, generalizable AI applications in diagnostics.
This challenge highlights the critical need for AI models in computational pathology to move beyond narrow datasets towards real-world clinical applicability across diverse biological and contextual scenarios.
The focus shifts from siloed, scanner-specific AI models to generalizable AI systems capable of handling unprecedented biological variance, which is crucial for widespread clinical adoption.
- · AI pathology companies
- · Diagnostic laboratories
- · Patients with complex conditions
- · Veterinary pathology
- · AI models lacking generalization
- · Traditional pathology workflows
- · Companies relying on brittle AI solutions
Improved accuracy and efficiency in cancer diagnosis and prognosis through advanced AI.
Increased adoption of AI in global healthcare systems for complex diagnostic tasks, reducing human error and workload.
Potential for new drug discovery and personalized medicine approaches driven by deeper, AI-powered pathological insights across diverse species.
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Read at arXiv cs.AI