In recent years, the accuracy of recognition methods using deep learning is much improved. Many people pay attention to the segmentation that class labels are assinged to all pixels in an image. So, we are doing research on the segmentation of cell membrane and nucleus in images. In previous research, we can not get high accuracy by deep learning in comparison with human because cell images include much noise and the shape of cell membrane is unstable. We would like to improve the robustness of noise and shape changes. This is the purpose of this research grant.
2017
堀田一弘 (2017) Deep learning を用いた細胞内画像中の粒子検出, 生体の科学, 68 (5): 470-471.
doi:10.11477/mf.2425200699.
Hasegawa R., Hotta K. (2017) PLSNet: Hierarchical Feature Extraction using Partial Least Squares Regression for Image Classification. IEEJ Transactions on Electrical and Electronic Engineering, 12 (S2): S91-S96.
doi: 10.1002/tee.22553.
高田大雅,堀田一弘 (2017) 三つのモデルの統合と適応的学習による群衆中の人追跡, 電気学会論文誌C, 137 (9): 1258-1265.
doi:10.1541/ieejeiss.137.1258.
Sato M., Hotta K., Imanishi A., Matsuda M., Terai K. (2018) Segmentation of Cell Membrane and Nucleus by Improving Pix2pix, International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS2018), 216-220.
doi:10.5220/0006648302160220.
Murata T., Hotta K., Imanishi A., Matsuda M., Terai K., (2018) Segmentation of Cell Membrane and Nucleus Using Branches with Different Roles in Deep Neural Network, International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS2018), 256-261.
doi:10.5220/0006717002560261.