| A collaborative workflow between pathologists and deep learning for the evaluation of tumour cellularity in lung adenocarcinoma T Sakamoto, T Furukawa, HHN Pham, K Kuroda, K Tabata, Y Kashima, ... Histopathology 81 (6), 758-769, 2022 | 13 | 2022 |
| NKG2A inhibits the anti-tumor CD8 T cell immune response elicited by radiotherapy NG Battaglia, JJ Caldon, EN Okoshi, SA Gerber, EM Lord The Journal of Immunology 204 (1_Supplement), 241.24-241.24, 2020 | 3 | 2020 |
| On the adoption of preprints in pathology research E Okoshi, J Fukuoka, A Bychkov Archives of Pathology & Laboratory Medicine 145 (12), 1477-1478, 2021 | 1 | 2021 |
| Differential Gene Expression of Tumors Undergoing Lepidic-Acinar Transition in Lung Adenocarcinoma EN Okoshi, S Fujita, K Lami, Y Kitamura, R Matsuda, T Miyazaki, ... medRxiv, 2024.03. 18.24304449, 2024 | | 2024 |
| Machine-Learning-Based Classification Model to Address Diagnostic Challenges in Transbronchial Lung Biopsy H Sano, EN Okoshi, Y Tachibana, T Tanaka, K Lami, W Uegami, Y Ohta, ... Cancers 16 (4), 731, 2024 | | 2024 |
| Focal Usual Interstitial Pneumonia-like Fibrosis is a Core Prognostic Factor in Progressive Pulmonary Fibrosis Y Tsushima, EN Okoshi, S Ishijima, A Bychkov, K Lami, S Morimoto, ... medRxiv, 2023.12. 07.23298650, 2023 | | 2023 |
| Distribution of UIP Fibrosis Is Accentuated Around Lymphatics-Analysis by a Deep Learning Model Y Nei, W Uegami, E Okoshi, J Fukuoka A69. AN IMAGE'S WORTH: STUDIES IN LUNG IMAGING, A2318-A2318, 2023 | | 2023 |
| Developing an explainable AI model for diagnosis and prognosis in interstitial lung disease W Uegami, K Uehara, A Bychkov, M Ozasa, EN Okoshi, T Johkoh, ... Journal of Pathology Informatics 13, 100070, 2022 | | 2022 |
