RapidMitDet: Boosting Computational Efficiency for Mitosis Detection via Magnification Alignment

Abstract

Automated mitosis detection is a critical research focus, enabling pathologists to rapidly assess mitotic indices for tumor malignancy evaluation. However, most of the existing detection approaches suffer from significant computational inefficiency due to the giga-pixel image resolution of Whole-Slide Images (WSIs), which greatly hinders their clinical applicability. In this study, we propose RapidMitDet, a computational efficient mitosis detection approach. Our method employs a coarse-to-fine framework, first identifying mitotic candidates at lower magnification and then confirming them at higher magnification. In the coarse-detection stage, we introduce magnification-aligned knowledge distillation to reduce the information gap between magnification levels while minimizing the performance degradation in the lower magnification detection model. In the subsequent fine-classification stage, we propose adaptive triplet learning (ATL) to optimize infomative sample selection, thereby enhancing discrimination between mitotic and non-mitotic instances. Experimental results show that RapidMitDet achieves the optimal accuracy-efficiency balance at 20x magnification, improving computational speed by up to 5.62 times over the latest state-of-the-art (SOTA) methods. Due to its lightweight model design, RapidMitDet can also deliver efficient detection at higher magnification (40x) while achieving even higher detection performance. In WSI-level evaluations, our method shows strong agreement with pathologists' diagnoses (ICCs: 0.879 (40x), 0.822 (20x)) while maintaining superior efficiency, highlighting its potential for clinical application. Code is available at https://github.com/logincat/RapidMitDet