Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025
This work addresses domain generalization in computational pathology for medical professionals, but it is incremental as it builds on existing deep learning methods.
The paper tackled mitotic figure detection and atypical mitosis classification in the MIDOG 2025 Challenge by proposing a two-stage detection-classification framework and an ensemble strategy, achieving effective results as demonstrated in experiments.
Deep learning has driven significant advances in mitotic figure analysis within computational pathology. In this paper, we present our approach to the Mitosis Domain Generalization (MIDOG) 2025 Challenge, which consists of two distinct tasks, i.e., mitotic figure detection and atypical mitosis classification. For the mitotic figure detection task, we propose a two-stage detection-classification framework that first localizes candidate mitotic figures and subsequently refines the predictions using a dedicated classification module. For the atypical mitosis classification task, we employ an ensemble strategy that integrates predictions from multiple state-of-the-art deep learning architectures to improve robustness and accuracy. Extensive experiments demonstrate the effectiveness of our proposed methods across both tasks.