用大模型自动识别斑马鱼发育异常，准确率超90%。
Automated Detection of Abnormalities in Zebrafish Development
- 基于时空变换器模型，融合图像序列分析发育状态
- 在13万张图像上实现98%受孕率分类准确率
- 适合药物毒性筛查与生物医学自动化研究
斑马鱼胚胎因其光学透明性和与人类的遗传相似性，是药物发现的重要模型。然而，当前评估依赖人工检查，成本高且费时。尽管机器学习具备自动化潜力，但受限于缺乏全面数据集。为此，我们构建了一个大规模高分辨率显微图像序列数据集，涵盖对照组及化合物（3,4-二氯苯胺）暴露下的斑马鱼胚胎发育过程。该数据集包含专家在细粒度时间尺度上的标注，支持两项基准任务：(1) 受孕率分类，评估斑马鱼卵子活力（130,368张图像）；(2) 毒性评估，检测有毒暴露引发的畸形随时间变化（55,296张图像）。同时，我们提出了首个基于Transformer的基线模型，整合时空特征以早期预测发育异常。实验结果表明，模型在受孕率分类中达到98%准确率，在毒性评估中达92%。这些成果凸显了自动化方法在斑马鱼毒理分析中的潜力。
原文摘要 · Abstract (English)
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.
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