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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.10464 |
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| _version_ | 1866918494533058560 |
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| author | Sivaprasad, Sarath Wang, Hui-Po Jäckel, Anna-Lisa Baumann, Jonas Baumann, Carole Herrmann, Jennifer Fritz, Mario |
| author_facet | Sivaprasad, Sarath Wang, Hui-Po Jäckel, Anna-Lisa Baumann, Jonas Baumann, Carole Herrmann, Jennifer Fritz, Mario |
| contents | Zebrafish embryos are a valuable model for drug discovery due to their optical transparency and genetic similarity to humans. However, current evaluations rely on manual inspection, which is costly and labor-intensive. While machine learning offers automation potential, progress is limited by the lack of comprehensive datasets. To address this, we introduce a large-scale dataset of high-resolution microscopic image sequences capturing zebrafish embryonic development under both control conditions and exposure to compounds (3,4-dichloroaniline). This dataset, with expert annotations at fine-grained temporal levels, supports two benchmarking tasks: (1) fertility classification, assessing zebrafish egg viability (130,368 images), and (2) toxicity assessment, detecting malformations induced by toxic exposure over time (55,296 images). Alongside the dataset, we present the first transformer-based baseline model that integrates spatiotemporal features to predict developmental abnormalities at early stages. Experimental results present the model's effectiveness, achieving 98% accuracy in fertility classification and 92% in toxicity assessment. These findings underscore the potential of automated approaches to enhance zebrafish-based toxicity analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10464 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Automated Detection of Abnormalities in Zebrafish Development Sivaprasad, Sarath Wang, Hui-Po Jäckel, Anna-Lisa Baumann, Jonas Baumann, Carole Herrmann, Jennifer Fritz, Mario Computer Vision and Pattern Recognition Zebrafish embryos are a valuable model for drug discovery due to their optical transparency and genetic similarity to humans. However, current evaluations rely on manual inspection, which is costly and labor-intensive. While machine learning offers automation potential, progress is limited by the lack of comprehensive datasets. To address this, we introduce a large-scale dataset of high-resolution microscopic image sequences capturing zebrafish embryonic development under both control conditions and exposure to compounds (3,4-dichloroaniline). This dataset, with expert annotations at fine-grained temporal levels, supports two benchmarking tasks: (1) fertility classification, assessing zebrafish egg viability (130,368 images), and (2) toxicity assessment, detecting malformations induced by toxic exposure over time (55,296 images). Alongside the dataset, we present the first transformer-based baseline model that integrates spatiotemporal features to predict developmental abnormalities at early stages. Experimental results present the model's effectiveness, achieving 98% accuracy in fertility classification and 92% in toxicity assessment. These findings underscore the potential of automated approaches to enhance zebrafish-based toxicity analysis. |
| title | Automated Detection of Abnormalities in Zebrafish Development |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.10464 |