A. Oyama

发表

First application of the super-resolution imaging technique using a Compton camera

J. Kotoku, J. Kataoka, Shogo Sato, 2020 .

Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data

J. Kotoku, Ryohei Yamamoto, Maki Shinzawa, 2022, Scientific Reports.

High-statistics image generation from sparse radiation images by four types of machine-learning models

Jun Kataoka, Shogo Sato, J. Kotoku, 2020, Journal of Instrumentation.

Hepatic tumor classification using texture and topology analysis of non-contrast-enhanced three-dimensional T1-weighted MR images with a radiomics approach

S. Furui, Y. Hiraoka, I. Obayashi, 2019, Scientific Reports.

Classification of optical coherence tomography images using a capsule network

T. Tsuji, Yuta Hirose, Kohei Fujimori, 2020, BMC Ophthalmology.

Image quality improvement in cone-beam CT using the super-resolution technique

Takeshi Takata, Shinobu Kumagai, Norikazu Arai, 2018, Journal of radiation research.

Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data

J. Kotoku, Ryohei Yamamoto, Maki Shinzawa, 2022, Scientific Reports.

Author Correction: Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data

J. Kotoku, Ryohei Yamamoto, Maki Shinzawa, 2022, Scientific Reports.

Hepatic tumor classification using texture and topology analysis of non-contrast-enhanced three-dimensional T1-weighted MR images with a radiomics approach

S. Furui, Y. Hiraoka, I. Obayashi, 2019, Scientific Reports.

Machine-learning Application to Fermi-LAT Data: Sharpening All-sky Map and Emphasizing Variable Sources

J. Kotoku, J. Kataoka, Shogo Sato, 2021, The Astrophysical Journal.