###### Abstract
Lesion segmentation is a combination of object detection and segmentation. The most well-known segmentation study used the U-net structure, a model architecture that can make use of the entire image context, and the decoder of U-net uses the encoder information by concatenating the encoder feature [10]. Some studies have combined lesion segmentation with classification. For example, a study on multi-task learning with a two-stream structure performed lesion segmentation and classification simultaneously [11]. In this study, to overcome the lack of data for these tasks, transfer learning approaches are proposed as follows, showing how each approach can be applied to each task: (1) transfer learning using weights learned from similar images; (2) transfer learning through a new type of patch self-supervised learning, a method that can be applied to unlabeled data; and (3) transfer learning using weights learned to distinguish RoIs. The method in (1) assumes that the model performance can be improved by transfer learning  
Figure 1: Diagram of medical image analysis problems. using the weights of the model trained with an image similar to the original task because of the lack of a dataset. For (2), a novel type of patch self-supervised learning is proposed to learn information from the image itself to use unlabeled data because of the prohibitive cost of target labeling. Although self-supervised learning is well-known in computer vision, patch self-supervised learning is newly presented to apply to the three target problems (i.e., classification, detection, and segmentation tasks) rather than a single classification problem with medical image analysis in this study. In addition, the cost of lesion and cancer labeling (e.g., abnormal/normal labeling) is very high because skilled clinicians are required. To eliminate this cost, an RoI is identified to be intensively viewed in the image, and because such RoI labeling is a simple task, it is less expensive than the actual target labeling. Therefore, for (3), a transfer learning method using only the image RoI is needed. The weight of the model trained on RoIs is learned again on the target classification. Furthermore, inspired by a clinician's diagnosis focusing on the RoI of the image, multi-task learning is proposed. Finally, to improve the performance, ensemble learning using appropriate transfer learning approaches for each task is introduced. We deliberately designed experiments with the limited labeled dataset to solve the above-mentioned problem, and the approaches were experimentally validated with cervical cancer classification, skin lesion detection and classification, and pressure ulcer segmentation, representative applications in medical image analysis. The following were observed from these experiments. The image classification and object classification tasks determine where to look in the image to assess the disease. Therefore, transfer learning using RoIs is effective for these tasks. Moreover, for the task of determining an overall shape, such as segmentation, transferring knowledge with a similar image is effective. The contributions of this study are as follows:  
* Because of the lack of data in medical images, we propose transfer learning approaches: (1) transfer learning with similar images, (2) a new approach using transfer learning with self-supervised learning, and (3) transfer learning with RoIs. We apply these approaches to image classification, object detection, and segmentation tasks, respectively, in representative medical images and demonstrate their performance. * In addition to these approaches, we propose a multi-task learning and ensemble approach using the optimal transfer learning method appropriate to medical images.