Wang Baihua, Sun Qi, Liu Yujia, Zhang Jiheng, Li Gaozheng, Wu Sifang, Zheng Houbing, Ye Jialin, Zhou Meihua, Zheng Haisu, Yu Yongqiang, Zhong Yi, Wu Yuanzi, Huang Da, Wang Biao, Weng Zuquan
College of Biological Science and Engineering, Fuzhou University, Fuzhou, Fujian, China.
The Center for Big Data Research in Burns and Trauma, College of Computer and Data Science/College of Software, Fuzhou University, Fujian, China.
Comput Biol Med. 2025 Apr;188:109892. doi: 10.1016/j.compbiomed.2025.109892. Epub 2025 Feb 25.
Larval zebrafish phenotypes serve as critical research indicators in fields such as ecotoxicology and safety assessment since phenotypic defects are closely related to alterations of underlying pathway. However, identifying these defects is time-consuming and requires specialized knowledge.
We proposed a deep network model called RECNet, which combines attention mechanisms and residual structures. In terms of data processing, we applied the mixup data augmentation technique and accumulated a collection of 6805 larval zebrafish phenotype images, mostly generated from our laboratory. Our proposed model was deployed to execute two distinct tasks, including a four-classification of zebrafish phenotypes and a seven-classification involving mixed labels for abnormalities.
In the four-class classification task, the RECNet model achieved an accuracy of 0.949, with a mean area under the curve of 0.986 and an F1-score of 0.966. Through interpretable research, attention mechanisms enable the model to focus more accurately on regions of interest. In the mixed-label seven-classification task for anomalies, our model achieved an accuracy of 0.913 and a mean average precision value of 0.847 by employing the weighted loss function (DFBLoss). Furthermore, in a new test dataset, the RECNet model achieved accuracy rates of 0.924 and 0.876 for the two tasks, respectively. Our RECNet model was trained by orders of magnitude larger dataset than previous studies and also showed better accuracy rates.
Our method holds promise for diverse applications within zebrafish laboratories and fields such as toxicology, providing indispensable support to scientific research.
由于表型缺陷与潜在通路的改变密切相关，斑马鱼幼体的表型在生态毒理学和安全性评估等领域是关键的研究指标。然而，识别这些缺陷既耗时又需要专业知识。
我们提出了一种名为RECNet的深度网络模型，它结合了注意力机制和残差结构。在数据处理方面，我们应用了混合数据增强技术，并积累了一个包含6805张斑马鱼幼体表型图像的数据集，大部分图像由我们实验室生成。我们提出的模型被用于执行两项不同的任务，包括斑马鱼表型的四类分类以及涉及异常混合标签的七类分类。
在四类分类任务中，RECNet模型的准确率达到0.949，曲线下平均面积为0.986，F1分数为0.966。通过可解释性研究，注意力机制使模型能够更准确地聚焦于感兴趣的区域。在异常的混合标签七类分类任务中，我们的模型通过采用加权损失函数（DFBLoss），准确率达到0.913，平均精度值为0.847。此外，在一个新的测试数据集中，RECNet模型在这两项任务中的准确率分别达到0.924和0.876。我们的RECNet模型是在比以往研究大几个数量级的数据集上进行训练的，并且也表现出了更高的准确率。
我们的方法在斑马鱼实验室以及毒理学等领域具有多种应用前景，为科学研究提供了不可或缺的支持。
