MIDOG 2025 Track 2：一种在类别和难度不平衡下对非典型与正常有丝分裂象进行分类的深度学习模型
MIDOG 2025 Track 2: A Deep Learning Model for Classification of Atypical and Normal Mitotic Figures under Class and Hardness Imbalances
AI总结：
针对数字病理学中有丝分裂象分类的类别与难度不平衡问题，提出基于ResNet且带有专用分类头的深度学习模型，结合focal loss与数据增强，在MIDOG 2025数据集上取得高平衡准确率，提升了预后评估的可靠性。
摘要：
动机：在数字病理学中，将有丝分裂象准确分类为正常和非典型类型对于肿瘤预后至关重要。然而，由于细微的形态学差异，以及真实世界组织病理学数据集中显著的类别和难度不平衡，为该任务开发鲁棒的深度学习模型具有挑战性。方法：我们提出了一种基于 ResNet 骨干网络并带有专用分类头的新型深度学习方法。我们的架构独特地同时对有丝分裂象表型和实例难度进行建模。该方法专门设计用于处理不同组织类型、扫描仪变异性和不平衡数据的挑战。我们采用了 focal loss 来有效缓解显著的类别不平衡，并实施了全面的数据增强流程以增强模型的鲁棒性和泛化能力。结果：我们的方法表现出强大且一致的性能。在 MIDOG 2025 Track 2 数据集上进行的 5 折交叉验证中，它实现了 0.8744 +/- 0.0093 的平均平衡准确率和 0.9505 +/- 0.029 的 ROC AUC。该模型在初步排行榜评估中表现出鲁棒的泛化能力，实现了 0.8736 +/- 0.0204 的总体平衡准确率。结论：所提出的方法为非典型和正常有丝分裂象的分类提供了一种可靠且可泛化的解决方案。通过解决真实世界数据的固有挑战，我们的方法有潜力支持临床实践中精确的预后评估，并提高病理诊断的一致性。
英文摘要：
Motivation: Accurate classification of mitotic figures into normal and atypical types is crucial for tumor prognostication in digital pathology. However, developing robust deep learning models for this task is challenging due to the subtle morphological differences, as well as significant class and hardness imbalances in real-world histopathology datasets. Methods: We propose a novel deep learning approach based on a ResNet backbone with specialized classification heads. Our architecture uniquely models both the mitotic figure phenotype and the instance difficulty simultaneously. This method is specifically designed to handle the challenges of diverse tissue types, scanner variability, and imbalanced data. We employed focal loss to effectively mitigate the pronounced class imbalance, and a comprehensive data augmentation pipeline was implemented to enhance the model's robustness and generalizability. Results: Our approach demonstrated strong and consistent performance. In a 5-fold cross-validation on the MIDOG 2025 Track 2 dataset, it achieved a mean balanced accuracy of 0.8744 +/- 0.0093 and an ROC AUC of 0.9505 +/- 0.029. The model showed robust generalization across preliminary leaderboard evaluations, achieving an overall balanced accuracy of 0.8736 +/- 0.0204. Conclusion: The proposed method offers a reliable and generalizable solution for the classification of atypical and normal mitotic figures. By addressing the inherent challenges of real world data, our approach has the potential to support precise prognostic assessments in clinical practice and improve consistency in pathological diagnosis.
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。
