In general, we need a lot of data for improving the accuracy of machine learning. However, the number of biological samples what we can obtain are not enough for machine learning. This problem exists in the classification of glomerular epithelial cells with the progress of disease, and its accuracy is contrary to our intuitive impression. Therefore, we would like to improve the accuracy by generating a lot of fake images using Generative Adversarial Nets (GANs). About podocyte cells, it was difficult to obtain an arbitrary disease by previous method. In this paper, we propose the model with restriction of learning by shapes information based on ACGANs, and we investigate how much fake images generated by our method are similar to real images. According to the results, the passage number of fake images by our method is 17% higher than conventional method.
Deriving accurate molecular indicators of protein synthesis through Raman-based sparse classification
