All-Transfer Learning for Deep Neural Networks and its Application to Sepsis Classification
This work addresses a domain-specific problem for medical image analysis, offering an incremental improvement in transfer learning for small datasets.
The authors tackled the problem of poor classification performance in deep neural networks when target domain data is scarce by proposing All-Transfer Deep Learning, which transfers all parameters and outperformed conventional methods in sepsis classification using 2-DE images.
In this article, we propose a transfer learning method for deep neural networks (DNNs). Deep learning has been widely used in many applications. However, applying deep learning is problematic when a large amount of training data are not available. One of the conventional methods for solving this problem is transfer learning for DNNs. In the field of image recognition, state-of-the-art transfer learning methods for DNNs re-use parameters trained on source domain data except for the output layer. However, this method may result in poor classification performance when the amount of target domain data is significantly small. To address this problem, we propose a method called All-Transfer Deep Learning, which enables the transfer of all parameters of a DNN. With this method, we can compute the relationship between the source and target labels by the source domain knowledge. We applied our method to actual two-dimensional electrophoresis image~(2-DE image) classification for determining if an individual suffers from sepsis; the first attempt to apply a classification approach to 2-DE images for proteomics, which has attracted considerable attention as an extension beyond genomics. The results suggest that our proposed method outperforms conventional transfer learning methods for DNNs.