| 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 | 666 | 2022 |
| 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 | 168 | 2020 |
| 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 | 159 | 2021 |
| 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 | 71 | 2019 |
| 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 | 49 | 2022 |
| Real-time processing of two-photon calcium imaging data including lateral motion artifact correction A Mitani, T Komiyama Frontiers in neuroinformatics 12, 98, 2018 | 46 | 2018 |
| Brain-computer interface with inhibitory neurons reveals subtype-specific strategies A Mitani, M Dong, T Komiyama Current Biology 28 (1), 77-83. e4, 2018 | 31 | 2018 |
| 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 | 18 | 2017 |
| 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 | 12 | 2011 |
| 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 | 10 | 2020 |
| 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 | 9 | 2013 |
| 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 | 5 | 2020 |
| 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, ... | 5 | 2019 |
| Retinal detection of kidney disease and diabetes A Mitani, N Hammel, Y Liu Nature biomedical engineering 5 (6), 487-489, 2021 | 4 | 2021 |
| 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 | 3 | 2020 |
| 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 | 2 | 2022 |
| 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 | 2 | 2022 |
| 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 | 1 | 2023 |
| 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 |
