Curtis P. Langlotz
Curtis P. Langlotz
Professor of Radiology, Medicine, and Biomedical Data Science, Stanford University
Verified email at - Homepage
Cited by
Cited by
Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning
P Rajpurkar, J Irvin, K Zhu, B Yang, H Mehta, T Duan, D Ding, A Bagul, ...
arXiv preprint arXiv:1711.05225, 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
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
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
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
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
RadLex: a new method for indexing online educational materials
CP Langlotz
Radiographics 26 (6), 1595-1597, 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
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
Breast MR imaging: interpretation model.
LW Nunes, MD Schnall, SG Orel, MG Hochman, CP Langlotz, ...
Radiology 202 (3), 833-841, 1997
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
Deep learning in neuroradiology
G Zaharchuk, E Gong, M Wintermark, D Rubin, CP Langlotz
American Journal of Neuroradiology 39 (10), 1776-1784, 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
Toward best practices in radiology reporting
CE Kahn Jr, CP Langlotz, ES Burnside, JA Carrino, DS Channin, ...
Radiology 252 (3), 852-856, 2009
Structured Reporting: Patient Care Enhancement or Productivity Nightmare?1
DL Weiss, CP Langlotz
Radiology 249 (3), 739-747, 2008
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
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
Diagnostic performance characteristics of architectural features revealed by high spatial-resolution MR imaging of the breast.
LW Nunes, MD Schnall, ES Siegelman, CP Langlotz, SG Orel, D Sullivan, ...
AJR. American journal of roentgenology 169 (2), 409-415, 1997
Acute appendicitis: comparison of helical CT diagnosis—focused technique with oral contrast material versus nonfocused technique with oral and intravenous contrast material
JE Jacobs, BA Birnbaum, M Macari, AJ Megibow, G Israel, DD Maki, ...
Radiology 220 (3), 683-690, 2001
Deep learning to classify radiology free-text reports
MC Chen, RL Ball, L Yang, N Moradzadeh, BE Chapman, DB Larson, ...
Radiology 286 (3), 845-852, 2018
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