用分层集成方法提升斑马鱼胚胎表型分类准确率
Hierarchical Specialised Ensembles for Classification of Zebrafish Phenotypes Using the Selected Image Recognition Methods
- 先用单分类器粗分四类，再对不确定样本用不同集成策略细分类
- 专用分层集成在F1分数上表现最佳，ConvNeXt模型整体性能最强
- 适合生物图像分析、高通量表型筛选场景的科研人员参考
本文提出并评估了三种用于斑马鱼表型分类的分层集成架构。第一阶段使用单一四分类器将胚胎图像归为四种互斥表型：正常、卵膜包裹、死亡或其它。其余类别图像进入第二阶段，根据不同架构采用单个多标签分类器、两个专用多标签分类器或二分类器集成方案。在ResNet18、ViT和ConvNeXt三种主干网络上进行比较，结果显示ConvNeXt在各架构下均表现最优，而第二套方案中的专用分层集成在F1分数上取得最佳平衡。结果表明，所提专用分层集成有效提升斑马鱼表型识别性能，且ConvNeXt是尤为合适的主干模型。
原文摘要 · Abstract (English)
We propose and evaluate three hierarchical ensemble setups for zebrafish phenotype classification from embryo images. In all setups, stage 1 uses a single four-class classifier to assign images to one of the exclusive phenotypes: Normal, Chorion, Dead, or Other. Images classified as Other are then processed in stage 2, where the ensemble design differs across setups: a single multi-label classifier, two specialized multi-label classifiers, or an ensemble of binary classifiers. We compare these setups using three backbone architectures: ResNet18, ViT, and ConvNeXt. Overall, ConvNeXt achieves the best performance across setups, while the specialized hierarchical ensemble in setup 2 provides the best balance in terms of F1-score. The results show that the proposed specialised hierarchical ensembles are effective for zebrafish phenotype recognition, and suggest that ConvNeXt is particularly useful backbone model.
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