###### Abstract  
A significant amount of research has investigated automating medical diagnosis using deep learning. However, because medical data are collected through diagnostic tests, deep learning methods used in existing studies have had a disadvantage in that the number of training samples is insufficient and the labeling cost is high. Training approaches considering the common characteristics of medical images are needed. Therefore, in this study, we investigated approaches to overcome the lack of data for representative medical imaging tasks using transfer learning technologies. The tasks were divided into image classification, object detection, and segmentation, commonly needed functions in medical image analyses. We proposed transfer learning approaches suitable for each task that can be applied when there are little medical image data available. These approaches were experimentally validated in the following applications that share similar issues of lacking data: cervical cancer classification (image classification), skin lesion detection and classification (object detection and classification), and pressure ulcer segmentation (segmentation). We also proposed multi-task learning and ensemble learning that can be applied to these applications. Finally, the approaches were compared with state-of-the-art results. In cervical cancer analysis, the performance was improved by 5.4% in sensitivity. Skin lesion classification showed improvement in accuracy of 8.7%, precision of 28.3%, and sensitivity of 39.7%. Finally, pressure ulcer segmentation improved in accuracy by 1.2%, intersection over union by 16.9%, and Dice similarity coefficient by 3.5%. medical image; transfer learning; patch self-supervised learning; multi-task learning; ensemble learning +
Footnote †: journal: Medical Image Sciences  
typically used in computer vision. Furthermore, unlabeled image data cannot be used in supervised learning. Therefore, in the classification task, due to the limited data, a transfer learning method is often used to extract visual features using the weights of a network pre-trained with the ImageNet dataset [6], and the weights are fine-tuned according to the task. One study divided the image into several patches and used it as an input to the model to capture local features from unlabeled data [7]. However, previous works used networks pre-trained with a large dataset, such as the ImageNet dataset [6], and then fine-tuned the networks to improve their performances, but they did not investigate the fact that networks pre-trained with images similar to the tasks might be better than networks pre-trained with the large dataset. An example of the detection task in medical image analysis is lesion detection. This task consists of locating areas of interest or identifying small lesions in an image, which is a key part of diagnosis. Creating labeled data for this task is the most labor-intensive task for clinicians. Therefore, because such manual labeling is frequently required, semi-supervised learning has been applied, as in [8]. In addition, the authors of [9] combined classification with lesion detection to improve the classification performance. That is, the authors attempted to diagnose a lesion after extracting the region of interest (RoI) from an image. Object detection is often used with classification, wherein the task is used to extract RoIs and classification is then performed using the extracted RoIs due to the lack of a training dataset. Finally, typical examples of the segmentation task are organ segmentation and lesion segmentation. Examples of organ segmentation include heart analysis and brain analysis requiring quantitative analysis of clinical parameters, such as the specific shape or volume, through the segmentation of organs or the organ's substructures. The quantitative analysis of organs is performed through magnetic resonance imaging (MRI) or radiography, but there is a lack of data because a limited number of patients undergo the examination.