## 2 Materials and Methods  
### Data Collection  
To train and develop an effective model for phenotype classification, the quality of the carefully collected dataset is crucial. Ground-truth datasets acquired and annotated by domain experts with the depiction of various classes are required [26]. Research based on publicly available ground-truth datasets has the advantage of enabling better evaluation and comparison of algorithms to enable continuous progress. Therefore, we chose the dataset from the [PERSON] et al. group which were produced and collected with rigorous processes [20].  
To obtain a collection of different defect phenotypes, [PERSON] et al. used increasing concentrations of varied chemicals for zebrafish embryo treatment, including propranolol, amiodarone, acetaminophen, valpro acid caffeine, theophylline 4,5-dichloroaniline, as well as heavy metals such as thallium, methylmercury, lead acetate and zinc sulfate. Phenotype images of control or treated embryos were captured in a lateral view with a high-resolution Olympus SZX10 microscope coupled with a camera and transmitted light illumination. The acquired images were in the size of \(2575\times 1932\) pixels.  
A sophisticated workflow of manual annotation was performed to build a high-quality ground-truth dataset. Three biologists labelled images with different categories of phenotypes by observations only in the first round. For each phenotype assigned to each image, the ground-truth was calculated by majority voting. Ten categories of phenotypes are identified, i.e., \"normal\", \"dead\", \"chorion\", \"down curved tail\", \"hemostasis\", \"necrosed yolk sac\", \"edema\", \"short tail\", \"up curved tail\" and \"up curved fish\". Sample images for different phenotypes are shown in Figure 1. By contrast with a regular classification dataset, there may be more than one phenotype assigned to a single image since more than one deformity may be found on one larva. Next, all annotations were reviewed by three biologists at the same time in order to reach agreement on phenotypes assigned to each image. After repeating the annotation and voting session, consensus was reached on 870 images. In total, 529 of them acquired in five independent acquisition days were set as a training dataset and other 341 images acquired in three additional acquisition days were integrated into the test dataset. The rigorous process of building the ground-truth dataset has laid the foundation of high-performance phenotype classification algorithms. Table 1 reports the number of training and testing images in this dataset. The training images are to be used for training the machine learning model for phenotype classification. The testing images are used for validating the trained model. The testing images are not included in the training images.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Dataset-** & **Normal** & **Dead** & \begin{tabular}{c} **Necrosed** \\ **Yolk Sac** \\ \end{tabular} & **Edema** & **Hemostasis** & \begin{tabular}{c} **Short** \\ **Tail** \\ \end{tabular} & \begin{tabular}{c} **Up** \\ **Curved** \\ **Tail** \\ \end{tabular} & \
\begin{tabular}{c} **Up** \\ **Curved** \\ **Fish** \\ \end{tabular} &
\begin{tabular}{c} **Chorion** \\ **Curved** \\ **Tail** \\ \end{tabular} \\ \hline \hline Training & 160 & 114 & 167 & 160 & 57 & 49 & 32 & 64 & 18 & 11 \\ Testing & 82 & 53 & 11 & 54 & 83 & 149 & 17 & 13 & 5 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of training and testing zebrafish larvae microscopic images of each phenotype.