| 3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis Y Tokuoka, TG Yamada, D Mashiko, Z Ikeda, NF Hiroi, TJ Kobayashi, ... NPJ systems biology and applications 6 (1), 1-12, 2020 | 21 | 2020 |
| Predicting the future direction of cell movement with convolutional neural networks S Nishimoto, Y Tokuoka, TG Yamada, NF Hiroi, A Funahashi PloS one 14 (9), e0221245, 2019 | 14 | 2019 |
| Convolutional neural network-based instance segmentation algorithm to acquire quantitative criteria of early mouse development Y Tokuoka, TG Yamada, NF Hiroi, TJ Kobayashi, K Yamagata, ... BioRxiv, 324186, 2018 | 12 | 2018 |
| An inductive transfer learning approach using cycle-consistent adversarial domain adaptation with application to brain tumor segmentation Y Tokuoka, S Suzuki, Y Sugawara Proceedings of the 2019 6th international conference on biomedical and …, 2019 | 7 | 2019 |
| Direct cell counting using macro-scale smartphone images of cell aggregates C Imashiro, Y Tokuoka, K Kikuhara, TG Yamada, K Takemura, ... IEEE Access 8, 170033-170043, 2020 | 2 | 2020 |
| Deep learning-based algorithm for predicting the live birth potential of mouse embryos Y Tokuoka, TG Yamada, D Mashiko, Z Ikeda, TJ Kobayashi, K Yamagata, ... bioRxiv, 2021 | 1 | 2021 |
| Deep learning for non-invasive determination of the differentiation status of human neuronal cells by using phase-contrast photomicrographs M Ooka, Y Tokuoka, S Nishimoto, NF Hiroi, TG Yamada, A Funahashi Applied Sciences 9 (24), 5503, 2019 | 1 | 2019 |
| Symbolic integration by integrating learning models with different strengths and weaknesses H Kubota, Y Tokuoka, TG Yamada, A Funahashi IEEE Access 10, 47000-47010, 2022 | | 2022 |
Akira FunahashiProfessor of Systems Biology, Keio University確認したメール アドレス: bio.keio.ac.jp
