ZFIN Image: Hark et al., 2025, Fig. 1

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Figure Caption

Fig. 1

Overview of the AI-based classification pipeline. (A) Images from multiple experiments are collected. A subset of 100 images was selected for manual human classification. k-fold cross-validation is performed. (B) The dataset is augmented and normalized. The BEiT model is trained using k-fold cross-validation (C) Inference is performed on the corresponding test dataset. Images were classified into three genotype classes: wildtype (WT), heterozygous (HET) and homozygous (HOM). Attention Rollout was applied to visualize the models decision making.

Acknowledgments

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