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Akinori Mitani
Akinori Mitani
Artera
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Title
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Cited by
Year
Underspecification presents challenges for credibility in modern machine learning
A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ...
Journal of Machine Learning Research 23 (226), 1-61, 2022
6612022
Detection of anaemia from retinal fundus images via deep learning
A Mitani, A Huang, S Venugopalan, GS Corrado, L Peng, DR Webster, ...
Nature Biomedical Engineering 4 (1), 18-27, 2020
1672020
Predicting the risk of developing diabetic retinopathy using deep learning
A Bora, S Balasubramanian, B Babenko, S Virmani, S Venugopalan, ...
The Lancet Digital Health 3 (1), e10-e19, 2021
1582021
Disengagement of motor cortex from movement control during long-term learning
EJ Hwang, JE Dahlen, YY Hu, K Aguilar, B Yu, M Mukundan, A Mitani, ...
Science advances 5 (10), eaay0001, 2019
712019
Detection of signs of disease in external photographs of the eyes via deep learning
B Babenko, A Mitani, I Traynis, N Kitade, P Singh, AY Maa, J Cuadros, ...
Nature biomedical engineering 6 (12), 1370-1383, 2022
472022
Real-time processing of two-photon calcium imaging data including lateral motion artifact correction
A Mitani, T Komiyama
Frontiers in neuroinformatics 12, 98, 2018
452018
Brain-computer interface with inhibitory neurons reveals subtype-specific strategies
A Mitani, M Dong, T Komiyama
Current Biology 28 (1), 77-83. e4, 2018
312018
Whisker row deprivation affects the flow of sensory information through rat barrel cortex
V Jacob, A Mitani, T Toyoizumi, K Fox
Journal of Neurophysiology 117 (1), 4-17, 2017
182017
Underspecification presents challenges for credibility in modern machine learning, 2020
A D’Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ...
arXiv preprint arXiv:2011.03395, 2011
122011
Underspecification presents challenges for credibility in modern machine learning. arXiv
A D’Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ...
arXiv preprint arXiv:2011.03395, 2020
102020
A leaky-integrator model as a control mechanism underlying flexible decision making during task switching
A Mitani, R Sasaki, M Oizumi, T Uka
PloS one 8 (3), e59670, 2013
92013
Improving medical annotation quality to decrease labeling burden using stratified noisy cross-validation
J Hsu, S Phene, A Mitani, J Luo, N Hammel, J Krause, R Sayres
arXiv preprint arXiv:2009.10858, 2020
52020
Disengagement of motor cortex from movement control during long-term learning. Sci Adv 5: eaay0001
EJ Hwang, JE Dahlen, YY Hu, K Aguilar, B Yu, M Mukundan, A Mitani, ...
52019
Retinal detection of kidney disease and diabetes
A Mitani, N Hammel, Y Liu
Nature biomedical engineering 5 (6), 487-489, 2021
42021
Detecting hidden signs of diabetes in external eye photographs
B Babenko, A Mitani, I Traynis, N Kitade, P Singh, A Maa, J Cuadros, ...
arXiv preprint arXiv:2011.11732, 2020
32020
Task-specific employment of sensory signals underlies rapid task switching
R Sasaki, H Kumano, A Mitani, Y Suda, T Uka
Cerebral Cortex 32 (21), 4657-4670, 2022
22022
Retinal fundus photographs capture hemoglobin loss after blood donation
A Mitani, I Traynis, P Singh, GS Corrado, DR Webster, LH Peng, ...
medRxiv, 2021.12. 30.21268488, 2022
22022
Machine Learning for Detection of Diseases from External Anterior Eye Images
Y Liu, N Hammel, A Mitani, DJ Wu, AD Bora, AV Varadarajan, ...
US Patent App. 18/011,597, 2023
12023
Processing fundus images using machine learning models to generate blood-related predictions
C Semturs, DR Webster, AV Varadarajan, A Mitani, LHY Peng
US Patent 11,823,385, 2023
2023
Beyond Predictions: Explainability and Learning from Machine Learning
CY Deng, A Mitani, CW Chen, LH Peng, N Hammel, Y Liu
Digital Eye Care and Teleophthalmology: A Practical Guide to Applications …, 2023
2023
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