Lili Zhai | Searcharxiv

arXiv2026

RapidMitDet: Boosting Computational Efficiency for Mitosis Detection via Magnification Alignment

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

eess.IV↗

arXiv2026

BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathology

Generalist pathology foundation models (PFMs), pretrained on large-scale multi-organ datasets, have demonstrated remarkable predictive capabilities across diverse clinical applications. However, their proficiency on the full spectrum of clinically essential tasks within a specific organ system remains an open question due to the lack of large-scale validation cohorts for a single organ as well as the absence of a tailored training paradigm that can effectively translate broad histomorphological knowledge into the organ-specific expertise required for specialist-level interpretation. In this study, we propose BRIGHT, the first PFM specifically designed for breast pathology, trained on over 51,000 breast whole-slide images derived from a cohort of over 40,000 patients across 19 hospitals. BRIGHT employs a collaborative generalist-specialist framework to capture both universal and organ-specific features. To comprehensively evaluate the performance of PFMs on breast oncology, we curate the largest multi-institutional cohorts to date for downstream task development and evaluation, comprising over 25,000 WSIs across 10 hospitals. The validation cohorts cover the full spectrum of breast pathology across 25 distinct clinical tasks spanning diagnosis, biomarker prediction, treatment response and survival prediction. Extensive experiments demonstrate that BRIGHT outperforms five leading generalist PFMs, achieving state-of-the-art (SOTA) performance in 25 of 25 internal validation tasks and in 4 of 11 external validation tasks with excellent heatmap interpretability. By evaluating on large-scale validation cohorts, this study not only demonstrates BRIGHT's clinical utility in breast oncology but also validates a collaborative generalist-specialist paradigm, providing a scalable template for developing PFMs on a specific organ system, accelerating the translation of foundation models into ...

cs.CV↗