🤖 AI Summary
This study addresses the challenge of fine-grained classification within the ambiguous “Other” phenotype category in zebrafish embryo images by proposing a two-stage hierarchical ensemble approach. In the first stage, a four-class model identifies major phenotypes; in the second stage, three specialized ensemble architectures further refine the “Other” class, with Setup 2—incorporating a multi-label classifier—demonstrating superior performance and better class balance. Experiments conducted using ResNet18, Vision Transformer (ViT), and ConvNeXt backbones reveal that ConvNeXt substantially enhances feature representation and consistently achieves the best results across all configurations, thereby validating the efficacy and advancement of the proposed hierarchical ensemble strategy.
📝 Abstract
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.