## 4 Experiments  
We experimented with the proposed approaches for the following three applications: (1) cervical cancer classification, (2) skin lesion classification, and (3) pressure ulcer segmentation. The sizes of the images used for training were different in this experiment. However, a consistent image size was needed for each application. Therefore, all image sizes were empirically set to 512 x 512 pixels for cervical cancer classification, 448 x 448 for skin lesion classification, and 224 x 224 for pressure ulcer segmentation. In addition, in the pressure ulcer images, the pixel brightness was adjusted in pre-processing to make the ulcer boundary clearly visible through the anti-aliasing technique. We also used data augmentation, such as rotation and flip, to increase the number of training samples for the applications. In addition, 70% and 20% of the data were randomly selected for training and validation, respectively, while the remaining 10% was selected for testing. We kept the ratio between classes roughly the same for all three parts.  
The \(T_{sim}\) experiment was performed on the baseline model for each application with similar images, and the model was fine-tuned for the target dataset. Therefore, in this experiment, the performance for the three applications was measured using the weight of the transferred model to be trained with similar images.  
The \(T_{ssl}\) experiment used the novel patch self-supervised learning that learned self-information in the image, and it was assumed that the performance would increase if the transferred model with unsupervised learning was fine-tuned to fit the application. Therefore, in this experiment, transfer learning with self-supervised learning to predict the arrangement of nine shuffled patches of an image was used for the three applications. Cervical cancer classification had unlabeled images, so the approach was performed with these images, whereas skin lesion classification and pressure ulcer segmentation were trained with images with the labels removed for consistency.  
The \(T_{roi}\) experiment assumed that performance would increase if the transferred model used to segment only the important area (the RoI) in the image was fine-tuned to the application. Therefore, in this experiment, the baseline model was transformed into a U-Net model by attaching the decoder, and the model was trained to segment the RoI. After training, the U-Net encoder was fine-tuned, and the performance was measured. However, because pressure ulcer segmentation was a targeted task, RoI training was replaced by classifying the pressure ulcers in the dataset, including wounds and ulcer images. The encoder baseline model U-Net for pressure ulcer segmentation was extracted and used to train the classification. The transferred model was transformed into a U-Net model to segment the pressure ulcers.  
When making a diagnosis, physicians examine important parts of the image simultaneously to diagnose a lesion or cancer. Therefore, multi-task learning was carried out inspired by the idea of RoI detection and classification being performed simultaneously [88]. In each application experiment, the model consisted of two branches: segmentation and classification. The classification branch was the baseline model of the application, and the segmentation branch was the U-Net model, which segmented the RoI by attaching an up-sampling layer from the extracted visual features of the baseline model. Furthermore, the weight of the loss of the two branches was adjusted using Equation (8). In addition, the training of the baseline model of each application was based on the following three models (application tasks): ResNet50 (cervical cancer classification), DenseNet121 (skin lesion classification), and ResNet18 (pressure ulcer segmentation). The baseline models selected empirically were trained on the ImageNet dataset and then fine-tuned with the application datasets.