###### Abstract
Until now, at the research level, there have been researches on image processing of two-dimensional electrophoresis using AI, but they have not yet been put into their practical uses [8]. In this research, using the dramatic progress of AI in recent years, two-dimensional electrophoresis image processing by AI for practical use has been studied. Another major task was to obtain sufficient training data for machine learning. In already practically developed AI image diagnosis (recognition) methods, several hundred-thousand to several million images are used as training data. In this study, we achieved high throughput by improving the 2D-GE method using less than one-thousand images for training data. A further major task is to effectively achieve machine learning with a smaller amount of training data. In this article, sepsis classification was performed using deep neural network (DNN) which has been widely studied in recent years [9]. DNN is known to require a large amount of training data and has achieved high classification performance using large dataset such as skin cancer [10]. On the other hand, it is still difficult to collect sufficient number of the proteome data of patient due to a 2D-GE method because of the conventional technical issues that the time to obtain data are too long and the quality of data to be compared with each other is too low. Because of the issues mentioned above, the technique could be used only for research settings, and the practical implementation has been difficult so far. In fact, despite using our 2D-GE method, we could only collect 98 images which include 30 sepsis images (68 images are non-sepsis images). From this situation, we applied a transfer learning approach for DNNs. In order to solve the problem, it is necessary not only to improve the algorithm but also to develop more general-purpose devices to generate the big-data, but this is another topic for other studies. Transfer learning is a method that reuses source domain knowledge to solve a target domain task [11]. For example, if we want to solve a task that determines whether a patient of an input image has sepsis or not by reusing the knowledge of natural pictures classification task (e.g., dog or cat), the natural pictures classification task corresponds to the source domain task, and the sepsis classification task corresponds to the target domain task. Transfer learning for DNN has been studied in various fields of AI [12; 13] and these methods can be divided into three approaches: supervised, semi-supervised, and unsupervised. Unsupervised and semi-supervised approaches assume that the target domain labels equal the source domain label [14]. However, in a biomedical field, it is difficult to collect target domain data having the same label as the source domain. Therefore, we focus on the supervised transfer learning approach, which allows the labels of the source/target domain to be different. Conventional supervised transfer learning methods for DNN [15] first train a DNN on the source domain. Then, the second DNN is constructed based on target domain data by reusing the hidden layers of the first DNN as the initial values, except for an output layer. However, this approach has a risk of poor classification performance caused by random initial values of the output layer because the output layer must be trained on a significantly small amount of target domain data. To avoid this problem, we propose a reusing of all layers, including the output layer. An outline of our learning method is shown in Figure 1. First, we train a DNN (source DNN) to solve the source domain task (Figure 1A). In this study, we pre-trained hidden layers based on a de-noising autoencoder [4] and minimized a mean squared error between target vector and output vector using a stochastic gradient descent algorithm. Second, we reuse all parameters of the source DNN for solving the target domain task (Figure 1B). Finally, we fine-tuned all parameters using the target domain data and obtained a target DNN (Figure 1C).