## 4 Experiments
### Baselines  
In our experiments, we use the same baselines as in [19]. In addition, several methods related to capsule networks are used in the classification tasks of the seven datasets mentioned above.  
**ResNet18 and ResNet50 [6]**. These two models are trained for 100 epochs, using a cross-entropy loss function and an SGD optimizer with a bath size of 128 and an initial learning rate \(10^{-3}\).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **\#Training** & **\#Validation** & **\#Testing** \\ \hline Bladder & 1148 & 188 & 811 \\ \hline Femur-left & 637 & 104 & 439 \\ \hline Femur-right & 615 & 95 & 447 \\ \hline Heart & 721 & 246 & 510 \\ \hline Kidney-left & 1132 & 140 & 704 \\ \hline Kidney-right & 1119 & 159 & 693 \\ \hline Liver & 3464 & 491 & 2078 \\ \hline Lung-left & 741 & 261 & 397 \\ \hline Lung-right & 803 & 275 & 439 \\ \hline Pancreas & 2004 & 280 & 1343 \\ \hline Spleen & 1556 & 213 & 968 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Details of OrganMNIST_Sagittal dataset.  
**AutoML Methods.** Several AutoML methods [21, 22] were applied on MedMNIST classification. The experimental settings of three AutoML methods (auto-sklearn [21], AutoKeras [22] and Google AutoML Vision) are the same as in [19]. AutoML methods are designed to search for the optimal hyper-parameter setting or neural architecture to maximize the predictive ability. For example, Auto-sklearn and autoKeras are open-source AutoML tools for both statistical machine learning and deep learning. On the other hand, Google AutoML Vision offers commercial black-box AutoML tools. In this study, the results of AutoML methods in our experiments are directly referenced from [19].  
**CapsNet (Dynamic routing) [12].** In [12], the output feature maps of the convolutional layer with kernel size 9 \(\times\) 9 and a stride 2 are reshaped into primary capsules. The capsules of the previous layer are routed to classification capsules by agreement. Different from the original setting, we set the iteration number to be 2 instead of 3 for the best performance.  
**CapsNet (EM routing) [13].** In [13], the capsule contains a 4 \(\times\) 4 pose matrix and an activation. The vote for capsules in the next layer is computed by the matrix multiplication between the pose matrix and the trainable transformation matrix. The routing coefficients between capsules and classification capsules are updated through the EM algorithms. Different from the original setting, the kernel size of the first convolutional layer is set to be 3 \(\times\) 3 for detailed feature representation.  
**MS-Caps [14].** In [14], hierarchical features are extracted and reshaped into capsules of different dimensions. The capsules cascade together for the dropout operation. Following the original experimental setting, the Adam optimizing method [38] is used as the gradient descent algorithm to perform the training. The weight decay factor is set to be 0.00001. The initial learning rate is 0.001 and 0.0001, and the number of iterations is 25 and 50 for converging to the optimal solution quickly.  
**DeepCaps [25].** In [25], the Adam optimizer is used with an initial learning rate of 0.001, which is reduced by half after every 20 epochs.