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Rajpurkar P, Yang J, Dass N, et al. Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With DepressionA Prespecified Secondary Analysis of a Randomized Clinical TrialJAMA Netw Open.2020 Jun 22;3(6):e206653. doi:10.1001/jamanetworkopen.2020.6653

Rajpurkar P, Irvin J, Langlotz CP, Ng AY, Patel BN. AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining. Sci Rep 10, 3958 (2020).

Larson DB, Magnus D, Lungren MP,  Shah NH, Langlotz CP. Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework. Radiology. 2020 Mar 24. doi:10.1148

Balachandar N, Chang K, Kalpathy-Cramer J, Rubin DL. Accounting for data variability in multi-institutional distributed deep learning for medical imaging. J Am Med Inform Assoc. 2020 Mar 20. pii: ocaa017. doi: 10.1093/jamia/ocaa017.

Zheng H, Momeni A, Cedoz PL, Vogel H, Gevaert O. Whole slide images refelect DNA methylation patterns of human tumors. NPJ Genom Med. 2020 Mar 10;5:11. doi: 10.1038/s41525-020-0120-9

El Kaffas A, Rubin DL, Kamaya A. Spatial Characterization of Tumor Perfusion Properties from 3D DCE-US Perfusion Maps are Early Predictors of Cancer Treatment Response. Sci Rep 10 6996 (2020).

Varma M, Lu M, Gardner R, et al. Automated abnormality detection in lower extremity radiographs using deep learning. Nat Mach Intell 1, 578–583 (2019) doi:10.1038/s42256-019-0126-0

Patel BN, Rosenberg L, Willcox G, et al. Human–machine partnership with artificial intelligence for chest radiograph diagnosis. npj Digit. Med. 2, 111 (2019) doi:10.1038/s41746-019-0189-7

Allen B Jr, Seltzer SE, Langlotz CP, Dreyer KP, Summers RM, Petrick N, Marinac-Dabic D, Cruz M, Alkasab TK, Hanisch RJ, Nilsen WJ, Burleson J, Lyman K, Kandarpa K. A Road Map for Translational Research on Artificial Intelligence in Medical Imaging: From the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop. J Am Coll Radiol. 2019 Sep;16(9 Pt A):1179-1189.

Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, Marklund H, Haghgoo B, Ball R, Shpanskaya K, Seekins J, Mong DA, Halabi SS, Sandberg JK, Jones R, Larson DB, Langlotz CP, Patel BN, Lungren MP, Ng AY. CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. AAAI. 2019;590-597.

Park A, Chute C, Rajpurkar P, Lou J, Ball RL, Shpanskaya K, Jabarkheel R, Kim LH, McKenna E, Tseng J, Ni J, Wishah F, Wittber F, Hong DS, Wilson TJ, Halabi S, Basu S, Patel BN, Lungren MP, Ng AY, Yeom KW. Deep Learning-Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model. JAMA Netw Open. 2019 Jun 5;2(6):e195600.

Langlotz CP, Allen B, Erickson BJ, Kalpathy-Cramer J, Bigelow K, Cook TS, Flanders AE, Lungren MP, Mendelson DS, Rudie JD, Wang G, Kandarpa K. A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop. Radiology. 2019 Jun;291(3):781-791.

Wang X, Zhang Y, Ren X, Zhang Y, Zitnik M, Shang J, Langlotz C, Han J. Cross-type biomedical named entity recognition with deep multi-task learning. 2019 May 15;35(10):1745-1752.

Dunnmon JA, Yi D, Langlotz CP, Ré C, Rubin DL, Lungren MP.  Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs. Radiology. 2019 Feb;290(2):537-544. 

Bien N, Rajpurkar P, Ball RL, Irvin J, Park A, Jones E, Bereket M, Patel BN, Yeom KW, Shpanskaya K, Halabi S, Zucker E, Fanton G, Amanatullah DF, Beaulieu CF, Riley GM, Stewart RJ, Blankenberg FG, Larson DB, Jones RH, Langlotz CP, Ng AY, Lungren MP. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLoS Med. 2018 Nov 27;15(11):e1002699.

Banerjee I, Ling Y, Chen MC, Hasan SA, Langlotz CP, Moradzadeh N, Chapman B, Amrhein T, Mong D, Rubin DL, Farri O, Lungren MP. Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. Artif Intell Med. 2018 Nov 23. pii: S0933-3657(17)30625-5.

Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz CP, Patel BN, Yeom KW, Shpanskaya K, Blankenberg FG, Seekins J, Amrhein TJ, Mong DA, Halabi SS, Zucker EJ, Ng AY, Lungren MP. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. Plos Med. 2018 Nov 20;15(11):e1002686.

Banerjee I, Gensheimer MF, Wood DJ, Henry S, Aggarwal S, Chang DT, Rubin DL. Probabilistic prognostic estimates of survival in metastatic cancer patients (PPES-Met) utilizing free-text clinical narratives. Sci Rep 2018 Jul 3;8(1):10037.

Rajpurkar P, Irvin J,  Zhu K, Ball R, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K, Lungren MP, Ng AY. MURA Dataset: Towards Radiologist-Level Abnormality Detection in Musculoskeletal Radiographs arXiv:1712.06957.

Banerjee I, Chen MC, Lungren MP, Rubin DL. Radiology Report Annotation using Intelligent Word Embeddings: Applied to Multi-institutional Chest CT Cohort. Journal of Biomedical Informatics.

Zhou M, Leung A, Echegaray S, Gentles A, Shrager JB, Jensen KC, Berry GJ, Plevritis SK, Rubin DL, Napel S, Gevaert O. Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications. Radiology. 2018 Jan;286(1):307-315.

Rajpurkar P, Irvin J,  Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K, Lungren MP, Ng AY.  CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv:1711.05225v1

Chen MC, Ball RL, Yang L, Moradzadeh N, Chapman B, Larson DB, Langlotz CP, Amrhein TJ, Lungren MP. Deep learning to Classify Radiology Free Text Reports. Radiology. 2017 Nov 13:171115.

Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology. 2017 Nov 2:170236.

Do BH, Langlotz C, Beaulieu CF. Bone Tumor Diagnosis Using a Naïve Bayesian Model of Demographic and Radiographic Features. J Digit Imaging. 2017 Oct;30(5):640-647

Wang S, Zhou M, Liu Z, Liu Z, Gu D, Zang Y, Dong D, Gevaert O, Tian J. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Med Image Anal. 2017 Aug;40:172-183.

Wang S, Zhou M, Gevaert O, Tang Z, Dong D, Liu Z, Tian J.  A multi-view deep convolutional neural networks for lung nodule segmentation. Proc IEEE Eng Med Biol Soc. 2017 Jul;2017:1752-1755.

Hassanpour S, Bay G, Langlotz CP. Characterization of Change and Significance for Clinical Findings in Radiology Reports Through Natural Language Processing. J Digit Imaging. 2017 Jun;30(3):314-322.

Hassanpour S, Langlotz CP, Amrhein TJ, Befera NT, Lungren MP. Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield. AJR Am J Roentgenol. 2017 Apr;208(4):750-753.

de Sisternes L Jonna G, Moss J, Marmor MF, Leng T, Rubin DL.  Automated intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes. Biomed Opt Express. 2017 Feb 28;8(3):1926-1949.

Echegaray S, Nair V, Kadoch M, Leung A, Rubin D, Gevaert O, Napel S. A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer. Tomography. 2016 Dec;2(4):283-294.

Hassanpour, S., Langlotz, C. P. Information extraction from multi-institutional radiology reports. Artif Intell Med. 2016 Jan;66:29-39.