Transfer Learning on Interstitial Lung Disease Classification

Abstract

For the treatment of Interstitial Lung Disease, it is crucial to have an early diagnosis. However, doctors still have a lot of controversy in the diagnosis of lung nodules even with today’s highly developed medical imaging technology. In this article, we summarized the five major challenges we face in medical image recognition and systematically listed the applications from traditional image recognition technology to deep learning in lung CT image recognition. Compared to the traditional convolutional neural network built and trained from scratch, it is beneficial to apply transfer learning to the recognition of lung nodules. Transfer learning focus on transferring knowledge from previous well-trained task to target learning task. Transferring means pretrained networks utilize fine-tuning to reduce iteration times of weight so that it can cope with the problem of lack of high quality images. Various experiments demonstrate that transfer learning performances better than traditional convolutional neural network under complicated circumstances of image recognition such as medical images. In this article, transfer learning is classified into 3 types: inductive transfer learning, transductive transfer learning and unsupervised transfer learning. The main difference between them is label quantity of target training set. Inductive transfer learning highly depends on feature engineering. Compared to it, training sets of two remaining has few labels. However, transductive transfer learning and unsupervised transfer learning are unstable while facing sophisticated cases.