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

arXiv: https://arxiv.org/abs/2606.07368 · HTML: https://mlsanity.com/paper/2606.07368/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 118 references that are in the map.
- First posted on arXiv 5 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [RTMDet: An Empirical Study of Designing Real-Time Object Detectors](https://mlsanity.com/paper/2212.07784/) (2022)
- [Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology](https://mlsanity.com/paper/2408.00738/) (2024)
- [Virchow: A Million-Slide Digital Pathology Foundation Model](https://mlsanity.com/paper/2309.07778/) (2023)
- [Hibou: A Family of Foundational Vision Transformers for Pathology](https://mlsanity.com/paper/2406.05074/) (2024)
- [Detecting Cancer Metastases on Gigapixel Pathology Images](https://mlsanity.com/paper/1703.02442/) (2017)
- [SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and Baseline Models](https://mlsanity.com/paper/2503.02876/) (2025)
- [RF-DETR Object Detection vs YOLOv12 : A Study of Transformer-based and CNN-based Architectures for Single-Class and Multi-Class Greenfruit Detection in Complex Orchard Environments Under Label Ambiguity](https://mlsanity.com/paper/2504.13099/) (2025)
- [KongNet: A Multi-headed Deep Learning Model for Detection and Classification of Nuclei in Histopathology Images](https://mlsanity.com/paper/2510.23559/) (2025)
- [Pan-Cancer mitotic figures detection and domain generalization: MIDOG 2025 Challenge](https://mlsanity.com/paper/2509.02585/) (2025)
- [MitoDetect++: A Domain-Robust Pipeline for Mitosis Detection and Atypical Subtyping](https://mlsanity.com/paper/2509.02586/) (2025)

## Built on by (most cited first, 1 of 1)

- [Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures](https://mlsanity.com/paper/2607.28007/) (2026)

## Abstract (arXiv)

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, digitized across multiple scanning platforms. Moving beyond hand-selected hotspots, the challenge required detection also in random tissue areas (representative of the whole slide detection situation) and challenging areas (areas rich in hard negatives). In the second track, we introduced the classification of atypical mitotic figures (AMFs). There were 18 teams submitting to the detection track, with F1 scores ranging up to 0.740. In the AMF detection track, we had 21 submissions with balanced accuracy values up to 0.908. Our analysis reveals that while most models perform reliably in traditional hotspots, significant performance degradation occurs in challenging ROIs, where false positive rates tripled. Furthermore, performance varied significantly across the 12 tumor types, highlighting "blind spots" in current state-of-the-art architectures when encountering rare or highly pleomorphic malignancies. Moreover, we evaluated the effectiveness of ensembling and found a mean increases of 1.5 and 1.3 percentage points in F1 score and balanced accuracy, respectively. In contrast, TTA showed no relevant improvement. MIDOG 2025 demonstrates that "in the wild" mitosis detection remains a significant hurdle. The transition from hotspot-only evaluation to a multi-contextual framework provides a more realistic proxy for clinical reliability.