## 4 Experiments  
### Datasets  
MedMNIST consists of 10 pre-processed datasets. It contains 10 open medical image datasets covering multiple tasks, including multi-class, binary classification, sequential regression, and multi-label. In our experiments, we focus on the multi-class tasks, such as PathMNIST, DermaMNIST, OCTMNIST, PneumoniaMNIST, OrganMNIST_Axial, OrganMNIST_Coronal, and OrganMNIST_Sagittal. In these seven datasets, the height and width of the images are resized to 28. Figure 2 demonstrates an overview of the seven datasets with samples. All datasets are divided into a training set, a validation set, and a test set. The number of images in each set is detailed in Table 1. The models are trained on the training sets, validated on the validation sets after each epoch during training, and finally evaluated on the test sets. **PathMNIST**. It is based on a prior study [34] for predicting survival from colorectal cancer histology slides, which provides a dataset of 100,000 non-overlapping image patches from hematoxylin and eosin-stained histological images, and a test dataset of 7180 images patches from a different clinical center. Nine types of tissues are involved, resulting in a multi-class classification task. The details of these nine categories are introduced in Table 2. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Name**} & **Data** & \multirow{2}{*}{**Tasks**} & \multirow{2}{*}{**\#Training**} & \multirow{2}{*}{**\#Validation**} & \multirow{2}{*}{**\#Test**} \\  & **Modality** & & & & \\ \hline PathMNIST & Pathology & Multi-Class & 89,996 & 10,004 & 7180 \\ \hline DermaMNIST & Dermatoscope & Multi-Class & 7007 & 1003 & 2005 \\ \hline OCTMNIST & OCT & Multi-Class & 97,477 & 10,832 & 1000 \\ \hline PneumoniaMNIST & Chest X-ray & Binary-Class & 4708 & 524 & 624 \\ \hline OrganMNIST\_Axial & Abdominal CT & Multi-Class & 34,581 & 6491 & 17,778 \\ \hline OrganMNIST\_Coronal & Abdominal CT & Multi-Class & 13,000 & 2392 & 8268 \\ \hline OrganMNIST\_Sagittal & Abdominal CT & Multi-Class & 13,940 & 2452 & 8829 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Datasets in MedMNIST used in our experiments. Figure 2: An Overview of the Datasets: PathMNIST, DermaMNIST, OCTMNIST, PneumoniaMNIST, OrganMNIST_Axial, OrganMNIST_Coronal, and OrganMNIST_Sagittal. **DernaMNIST**. It is based on HAM10000 [35], a large collection of multi-source dermatoscopic images of common pigmented skin lesions. The dataset consists of 10,015 images labeled as seven different categories, as a multi-class classification task. These seven categories are introduced in Table 3.  
**OctMNIST**. It is based on a prior dataset [36] of 109,309 valid optical coherence tomography images for retinal diseases. Four types are involved in this dataset, leading to a multi-class classification task. These four categories are introduced in Table 4.  
**PneumoniaMNIST**. It is based on a prior dataset [36] of 5856 pediatric chest X-ray images. This task is a binary class of pneumonia and normal. The source training set is split into training and validation sets with a ratio of 9:1, and its source validation set is used as the test set. These two categories are introduced in Table 5. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **\#Training** & **\#Validation** & **\#Testing** \\ \hline Choroidal Neovascularization & 33,484 & 3721 & 250 \\ \hline Diabetic Macular Edema & 10,213 & 1135 & 250 \\ \hline Drusen & 7754 & 862 & 250 \\ \hline Normal & 46,026 & 5114 & 250 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Details of OctMNIST dataset.