用ConvNeXt V2模型自动区分正常与异常有丝分裂图像
Automated Classification of Normal and Atypical Mitotic Figures Using ConvNeXt V2: MIDOG 2025 Track 2
- 基于ConvNeXt V2模型，结合中心裁剪预处理
- 在多类数据上实现高精度分类，克服类别不平衡问题
- 适合病理图像分析与医学影像智能诊断研究者
本文针对MIDOG 2025挑战赛第2赛道，提出一种二分类方法，用于识别组织病理图像中的正常有丝分裂（NMFs）与异常有丝分裂（AMFs）。方法采用ConvNeXt V2作为基础模型，结合60%中心裁剪预处理与五折交叉验证集成策略。针对严重类别不平衡、形态多样性高及不同肿瘤类型、物种和扫描设备带来的域异质性等关键挑战，通过混合精度训练提升模型鲁棒性。实验表明，该方案在多样化的MIDOG 2025数据集上表现优异，验证了现代卷积架构在有丝分裂亚型分类中的有效性，同时通过合理结构设计与训练优化保持高效计算性能。
原文摘要 · Abstract (English)
This paper presents our solution for the MIDOG 2025 Challenge Track 2, which focuses on binary classification of normal mitotic figures (NMFs) versus atypical mitotic figures (AMFs) in histopathological images. Our approach leverages a ConvNeXt V2 base model with center cropping preprocessing and 5-fold cross-validation ensemble strategy. The method addresses key challenges including severe class imbalance, high morphological variability, and domain heterogeneity across different tumor types, species, and scanners. Through strategic preprocessing with 60% center cropping and mixed precision training, our model achieved robust performance on the diverse MIDOG 2025 dataset. The solution demonstrates the effectiveness of modern convolutional architectures for mitotic figure subtyping while maintaining computational efficiency through careful architectural choices and training optimizations.
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