提出两阶段检测与集成分类法，提升癌细胞分裂检测与异常分类精度
Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025
- 两阶段框架先定位候选分裂核再精细分类
- 集成多模型预测，显著提升异常分类准确率
- 适用于病理图像分析中的分裂核识别与临床辅助诊断
深度学习推动了计算病理学中细胞分裂核分析的进展。本文介绍了参与MIDOG 2025挑战赛的方法，包含两个任务：分裂核检测和异常分裂核分类。针对检测任务，提出两阶段检测-分类框架，先定位候选分裂核，再通过专用分类模块优化结果；针对异常分类任务，采用集成策略融合多个先进深度学习模型的预测，增强鲁棒性与准确性。大量实验验证了所提方法在两项任务上的有效性。
原文摘要 · Abstract (English)
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.
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