基于DenseNet121的鲁棒非典型有丝分裂分类：面向域泛化的染色感知增强与混合损失
Robust Atypical Mitosis Classification with DenseNet121: Stain-Aware Augmentation and Hybrid Loss for Domain Generalization
AI总结：
本文提出基于DenseNet-121的非典型有丝分裂分类框架，结合染色感知增强与混合损失，在MIDOG 2025测试集上实现85.0%平衡准确率，展现出强域泛化能力。
摘要：
非典型有丝分裂像在组织病理学中是肿瘤侵袭性的重要生物标志物，但由于严重的类别不平衡和跨成像域的变异性，可靠识别仍具挑战性。我们提出了一个基于DenseNet-121的框架，专为MIDOG 2025（Track 2）环境下的非典型有丝分裂分类而设计。该方法整合了染色感知增强（Macenko）、几何与强度变换，以及通过加权采样实现的失衡感知学习，并采用结合了类别加权二元交叉熵和focal loss的混合目标。模型使用AdamW端到端训练，并在多个独立域上评估，在扫描仪和染色变化下表现出强泛化能力，在官方测试集上实现了85.0%的平衡准确率、0.927的AUROC、89.2%的敏感性和80.9%的特异性。结果表明，将DenseNet-121与染色感知增强及失衡自适应目标相结合，可产生一个鲁棒且域可泛化的框架，适用于真实世界的计算病理学工作流。
英文摘要：
Atypical mitotic figures are important biomarkers of tumor aggressiveness in histopathology, yet reliable recognition remains challenging due to severe class imbalance and variability across imaging domains. We present a DenseNet-121-based framework tailored for atypical mitosis classification in the MIDOG 2025 (Track 2) setting. Our method integrates stain-aware augmentation (Macenko), geometric and intensity transformations, and imbalance-aware learning via weighted sampling with a hybrid objective combining class-weighted binary cross-entropy and focal loss. Trained end-to-end with AdamW and evaluated across multiple independent domains, the model demonstrates strong generalization under scanner and staining shifts, achieving balanced accuracy 85.0%, AUROC 0.927, sensitivity 89.2%, and specificity 80.9% on the official test set. These results indicate that combining DenseNet-121 with stain-aware augmentation and imbalance-adaptive objectives yields a robust, domain-generalizable framework for atypical mitosis classification suitable for real-world computational pathology workflows.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
