An Investigation of Transfer Learning Approaches to Overcome Limited Labeled Data in Medical Image Analysis

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%.