|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
|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
|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
|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
|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
|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, ...
|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
|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