团队西伍德用多模型集成方法提升癌细胞分裂检测与异常分类准确率。
Team Westwood Solution for MIDOG 2025 Challenge: An Ensemble-CNN-Based Approach For Mitosis Detection And Classification
- 先用nnUNetV2高敏筛选候选，再融合三个CNN的预测结果
- 在测试集上实现0.6972的检测F1分数和0.8242的分类平衡准确率
- 适合医学图像分析、病理图像自动诊断的研究者参考
本文介绍团队西伍德在MItosis DOmain Generalization (MIDOG) 2025挑战赛中针对有丝分裂检测与非典型有丝分裂分类的解决方案。对于有丝分裂检测，采用nnUNetV2进行初始候选筛查，随后通过随机森林集成三个卷积神经网络（EfficientNet-b3、EfficientNet-b5和EfficientNetV2-s）的预测结果；对于非典型有丝分裂分类，使用另一随机森林集成EfficientNet-b3、EfficientNet-b5和InceptionV3的输出。在初步测试集上，第一项任务的F1得分为0.7450，第二项任务的平衡准确率为0.8722；在最终测试集上，对应指标分别为0.6972和0.8242。
原文摘要 · Abstract (English)
This abstract presents our solution (Team Westwood) for mitosis detection and atypical mitosis classification in the MItosis DOmain Generalization (MIDOG) 2025 challenge. For mitosis detection, we trained an nnUNetV2 for initial mitosis candidate screening with high sensitivity, followed by a random forest classifier ensembling predictions of three convolutional neural networks (CNNs): EfficientNet-b3, EfficientNet-b5, and EfficientNetV2-s. For the atypical mitosis classification, we trained another random forest classifier ensembling the predictions of three CNNs: EfficientNet-b3, EfficientNet-b5, and InceptionV3. On the preliminary test set, our solution achieved an F1 score of 0.7450 for track 1 mitosis detection, and a balanced accuracy of 0.8722 for track 2 atypical mitosis classification. On the final test set, our solution achieved an F1 score of 0.6972 for track 1 mitosis detection, and a balanced accuracy of 0.8242 for track 2 atypical mitosis classification.
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