Dirichlet Process-Guided Dynamic Filtering for Mitosis Detection with Single Point Supervision

Accurate detection of mitotic figures in breast histopathology images is central to tumor grading and prognostic assessment. Precise bounding-box annotation remains labor-intensive and variable because mitotic figures are small, morphologically diverse, and boundary-ambiguous. Point-level supervision reduces annotation cost but lacks scale information, making reliable pseudo-box generation essential. Existing point-supervised pipelines often use static or heuristic thresholds that may become unstable as teacher predictions and proposal-score distributions evolve. We propose DDFMitos-Net, a point-supervised teacher-student framework for distribution-aware proposal filtering. The framework learns initial scale priors from point-guided simulated masks, refines teacher-generated pseudo-boxes through Adaptive Multiple Instance Learning, and uses Distribution-based Dynamic Filtering to integrate classification confidence with point-centered spatial information. Adaptive thresholds are estimated with a truncated Dirichlet Process Mixture Model. Transformation-Scale Learning improves geometric consistency, and Center-Aware Domain Adaptation provides auxiliary scanner-aware feature alignment. On MITOS12, MITOS14, TUPAC16, and MIDOG21, DDFMitos-Net achieved repeated-run F1 scores of 0.837 ± 0.004, 0.716 ± 0.006, 0.785 ± 0.005, and 0.806 ± 0.004, respectively. These results indicate stable and competitive point-supervised mitosis detection using low-cost point-level annotations.