CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning P Rajpurkar ArXiv abs/1711 5225, 2017 | 3308 | 2017 |
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison J Irvin, P Rajpurkar, M Ko, Y Yu, S Ciurea-Ilcus, C Chute, H Marklund, ... Proceedings of the AAAI conference on artificial intelligence 33 (01), 590-597, 2019 | 2790 | 2019 |
Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists P Rajpurkar, J Irvin, RL Ball, K Zhu, B Yang, H Mehta, T Duan, D Ding, ... PLoS medicine 15 (11), e1002686, 2018 | 1255 | 2018 |
Video-based AI for beat-to-beat assessment of cardiac function D Ouyang, B He, A Ghorbani, N Yuan, J Ebinger, CP Langlotz, ... Nature 580 (7802), 252-256, 2020 | 742 | 2020 |
Contrastive learning of medical visual representations from paired images and text Y Zhang, H Jiang, Y Miura, CD Manning, CP Langlotz Machine Learning for Healthcare Conference, 2-25, 2022 | 717 | 2022 |
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet N Bien, P Rajpurkar, RL Ball, J Irvin, A Park, E Jones, M Bereket, BN Patel, ... PLoS medicine 15 (11), e1002699, 2018 | 683 | 2018 |
RadLex: a new method for indexing online educational materials CP Langlotz Radiographics 26 (6), 1595-1597, 2006 | 511 | 2006 |
Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs DB Larson, MC Chen, MP Lungren, SS Halabi, NV Stence, CP Langlotz Radiology 287 (1), 313-322, 2018 | 456 | 2018 |
A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH/RSNA/ACR/The Academy Workshop CP Langlotz, B Allen, BJ Erickson, J Kalpathy-Cramer, K Bigelow, ... Radiology 291 (3), 781-791, 2019 | 380 | 2019 |
Mura: Large dataset for abnormality detection in musculoskeletal radiographs P Rajpurkar, J Irvin, A Bagul, D Ding, T Duan, H Mehta, B Yang, K Zhu, ... arXiv preprint arXiv:1712.06957, 2017 | 364 | 2017 |
Breast MR imaging: interpretation model. LW Nunes, MD Schnall, SG Orel, MG Hochman, CP Langlotz, ... Radiology 202 (3), 833-841, 1997 | 363 | 1997 |
Deep learning in neuroradiology G Zaharchuk, E Gong, M Wintermark, D Rubin, CP Langlotz American Journal of Neuroradiology 39 (10), 1776-1784, 2018 | 340 | 2018 |
Cross-type biomedical named entity recognition with deep multi-task learning X Wang, Y Zhang, X Ren, Y Zhang, M Zitnik, J Shang, C Langlotz, J Han Bioinformatics 35 (10), 1745-1752, 2019 | 319 | 2019 |
Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification I Banerjee, Y Ling, MC Chen, SA Hasan, CP Langlotz, N Moradzadeh, ... Artificial intelligence in medicine 97, 79-88, 2019 | 287 | 2019 |
Toward best practices in radiology reporting CE Kahn Jr, CP Langlotz, ES Burnside, JA Carrino, DS Channin, ... Radiology 252 (3), 852-856, 2009 | 281 | 2009 |
Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning E Tiu, E Talius, P Patel, CP Langlotz, AY Ng, P Rajpurkar Nature Biomedical Engineering 6 (12), 1399-1406, 2022 | 261 | 2022 |
Structured Reporting: Patient Care Enhancement or Productivity Nightmare?1 DL Weiss, CP Langlotz Radiology 249 (3), 739-747, 2008 | 254 | 2008 |
Impact of a deep learning assistant on the histopathologic classification of liver cancer A Kiani, B Uyumazturk, P Rajpurkar, A Wang, R Gao, E Jones, Y Yu, ... NPJ digital medicine 3 (1), 23, 2020 | 238 | 2020 |
Diagnostic criteria for fatty infiltration of the liver on contrast-enhanced helical CT. JE Jacobs, BA Birnbaum, MA Shapiro, CP Langlotz, F Slosman, ... AJR. American journal of roentgenology 171 (3), 659-664, 1998 | 238 | 1998 |
Assessment of convolutional neural networks for automated classification of chest radiographs JA Dunnmon, D Yi, CP Langlotz, C Ré, DL Rubin, MP Lungren Radiology 290 (2), 537-544, 2019 | 237 | 2019 |