| The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification U Baid, S Ghodasara, S Mohan, M Bilello, E Calabrese, E Colak, ... arXiv preprint arXiv:2107.02314, 2021 | 510 | 2021 |
| Federated learning for predicting clinical outcomes in patients with COVID-19 I Dayan, HR Roth, A Zhong, A Harouni, A Gentili, AZ Abidin, A Liu, ... Nature medicine 27 (10), 1735-1743, 2021 | 443 | 2021 |
| The RSNA pediatric bone age machine learning challenge SS Halabi, LM Prevedello, J Kalpathy-Cramer, AB Mamonov, A Bilbily, ... Radiology 290 (2), 498-503, 2019 | 380 | 2019 |
| ChatGPT is shaping the future of medical writing but still requires human judgment FC Kitamura Radiology 307 (2), e230171, 2023 | 182 | 2023 |
| Federated learning for breast density classification: A real-world implementation HR Roth, K Chang, P Singh, N Neumark, W Li, V Gupta, S Gupta, L Qu, ... Domain Adaptation and Representation Transfer, and Distributed and …, 2020 | 176 | 2020 |
| Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge AE Flanders, LM Prevedello, G Shih, SS Halabi, J Kalpathy-Cramer, ... Radiology: Artificial Intelligence 2 (3), e190211, 2020 | 166 | 2020 |
| The RSNA International COVID-19 Open Radiology Database (RICORD) EB Tsai, S Simpson, MP Lungren, M Hershman, L Roshkovan, E Colak, ... Radiology 299 (1), E204-E213, 2021 | 133 | 2021 |
| Magician’s Corner: 9. Performance Metrics for Machine Learning Models BJ Erickson, F Kitamura Radiology: Artificial Intelligence 3 (3), e200126, 2021 | 121 | 2021 |
| Deep Learning in Neuroradiology: A Systematic Review of Current Algorithms and Approaches for the New Wave of Imaging Technology AD Yao, DL Cheng, I Pan, F Kitamura Radiology: Artificial Intelligence 2 (2), e190026, 2020 | 78 | 2020 |
| The RSNA Pulmonary Embolism CT Dataset E Colak, FC Kitamura, SB Hobbs, CC Wu, MP Lungren, LM Prevedello, ... Radiology: Artificial Intelligence 3 (2), e200254, 2021 | 76 | 2021 |
| Automated coronary calcium scoring using deep learning with multicenter external validation D Eng, C Chute, N Khandwala, P Rajpurkar, J Long, S Shleifer, ... NPJ digital medicine 4 (1), 88, 2021 | 74 | 2021 |
| Federated Learning used for predicting outcomes in SARS-COV-2 patients M Flores, I Dayan, H Roth, A Zhong, A Harouni, A Gentili, A Abidin, A Liu, ... Research Square, 2021 | 47 | 2021 |
| Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT EH Lee, J Zheng, E Colak, M Mohammadzadeh, G Houshmand, N Bevins, ... NPJ digital medicine 4 (1), 1-11, 2021 | 40 | 2021 |
| ‘Data from medical imaging data resource center (MIDRC)-RSNA international covid radiology database (RICORD) release 1C—Chest X-ray, covid+(MIDRC-RICORD-1C) EB Tsai, S Simpson, MP Lungren, M Hershman, L Roshkovan, E Colak, ... The Cancer Imaging Archive. DOI: https://doi. org/10.7937/91ah-v663, 2021 | 37 | 2021 |
| Using DICOM metadata for radiological image series categorization: a feasibility study on large clinical brain MRI datasets R Gauriau, C Bridge, L Chen, F Kitamura, NA Tenenholtz, JE Kirsch, ... Journal of digital imaging 33, 747-762, 2020 | 35 | 2020 |
| Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays S Gaube, H Suresh, M Raue, E Lermer, TK Koch, MFC Hudecek, ... Scientific Reports 13 (1), 1383, 2023 | 29 | 2023 |
| Artificial Intelligence as a tool for diagnosis in Digital Pathology Whole Slide Images: A Systematic Review JPM Rodriguez, R Rodriguez, VWK Silva, FC Kitamura, GCA Corradi, ... Journal of Pathology Informatics, 100138, 2022 | 21 | 2022 |
| The Type III Secretion System (T3SS)-Translocon of Atypical Enteropathogenic Escherichia coli (aEPEC) Can Mediate Adherence FF Dos Santos, D Yamamoto, CM Abe, JA Bryant, RT Hernandes, ... Frontiers in Microbiology 10, 1527, 2019 | 21 | 2019 |
| Trustworthiness of Artificial Intelligence Models in Radiology and the Role of Explainability FC Kitamura, O Marques Journal of the American College of Radiology 18 (8), 1160-1162, 2021 | 20 | 2021 |
| Machine learning model for predicting severity prognosis in patients infected with COVID-19: Study protocol from COVID-AI Brasil F Paiva Proença Lobo Lopes, FC Kitamura, GF Prado, PEA Kuriki, ... Plos one 16 (2), e0245384, 2021 | 15 | 2021 |
Nitamar AbdalaUNIFESPZweryfikowany adres z epm.br
