Publications

Johnson AEW, Pollard TJ, Greenbaum NR, Lungren MP, Deng C-ying, Peng Y, Lu Z, Mark RG, Berkowitz SJ, Horng S.  0.  MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs.

Comeau DC, Doğan RIslamaj, Ciccarese P, Cohen KBretonnel, Krallinger M, Leitner F, Lu Z, Peng Y, Rinaldi F, Torii M et al..  2013.  BioC: a minimalist approach to interoperability for biomedical text processing. Database (Oxford). 2013:bat064.

Comeau DC, Batista-Navarro RTheresa, Dai H-J, Doğan RIslamaj, Yepes AJimeno, Khare R, Lu Z, Marques H, Mattingly CJ, Neves M et al..  2014.  BioC interoperability track overview. Database (Oxford). 2014

Comeau DC, Batista-Navarro RTheresa, Dai H-J, Doğan RIslamaj, Yepes AJimeno, Khare R, Lu Z, Marques H, Mattingly CJ, Neves M et al..  2014.  BioC interoperability track overview. Database (Oxford). 2014

Wei C-H, Peng Y, Leaman R, Davis APeter, Mattingly CJ, Li J, Wiegers TC, Lu Z.  2015.  Overview of the Biocreative V chemical disease relation (CDR) task. Proceedings of the BioCreative V Workshop. :154-166.

Wei C-H, Peng Y, Leaman R, Davis APeter, Mattingly CJ, Li J, Wiegers TC, Lu Z.  2016.  Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task. Database (Oxford). 2016

Kim S, Doğan RIslamaj, Chatr-Aryamontri A, Chang CS, Oughtred R, Rust J, Batista-Navarro R, Carter J, Ananiadou S, Matos S et al..  2016.  BioCreative V BioC track overview: collaborative biocurator assistant task for BioGRID. Database (Oxford). 2016

Kim S, Doğan RIslamaj, Chatr-Aryamontri A, Chang CS, Oughtred R, Rust J, Batista-Navarro R, Carter J, Ananiadou S, Matos S et al..  2016.  BioCreative V BioC track overview: collaborative biocurator assistant task for BioGRID. Database (Oxford). 2016

Kim S, Doğan RIslamaj, Chatr-Aryamontri A, Chang CS, Oughtred R, Rust J, Batista-Navarro R, Carter J, Ananiadou S, Matos S et al..  2016.  BioCreative V BioC track overview: collaborative biocurator assistant task for BioGRID. Database (Oxford). 2016

Doğan RIslamaj, Chatr-aryamontri A, Kim S, Wei C-H, Peng Y, Comeau D, Lu Z.  2017.  BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations. BioNLP 2017. :171–175.

Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow P-M, Zietz M, Hoffman MM et al..  2018.  Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 15(141)

Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow P-M, Zietz M, Hoffman MM et al..  2018.  Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 15(141)

Du J, Chen Q, Peng Y, Xiang Y, Tao C, Lu Z.  2019.  ML-Net: multi-label classification of biomedical texts with deep neural networks. J Am Med Inform Assoc. 26(11):1279-1285.

Chen Q, Peng Y, Keenan T, Dharssi S, N EAgro, Wong WT, Chew EY, Lu Z.  2019.  A multi-task deep learning model for the classification of Age-related Macular Degeneration. AMIA Jt Summits Transl Sci Proc. 2019:505-514.

Keenan TDL, Chen Q, Peng Y, Domalpally A, Agrón E, Hwang CK, Thavikulwat AT, Lee DH, Li D, Wong WT et al..  2020.  Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2. Ophthalmology.

Keenan TDL, Chen Q, Peng Y, Domalpally A, Agrón E, Hwang CK, Thavikulwat ATherese, Lee DHana, Li D, Wong WT et al..  2020.  Deep learning automated detection of reticular pseudodrusen from fundus autofluorescence images and color fundus photographs in the Age-Related Eye Disease Study 2 (AREDS2) . Investigative Ophthalmology & Visual Science. 61:1644.

Chen Q, Keenan TDL, Allot A, Peng Y, Agrón E, Domalpally A, Klaver CCW, Luttikhuizen DT, Colyer MH, Cukras CA et al..  2021.  Multimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration.. J Am Med Inform Assoc.

Han Y, Chen C, Tewfik AH, Ding Y, Peng Y.  2021.  Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning. IEEE International Symposium on Biomedical Imaging (ISBI).

Jaiswal A, Tang L, Ghosh M, Rousseau J, Peng Y, Ding Y.  2021.  RadBERT-CL: Factually-Aware Contrastive Learning for Radiology Report Classification. Machine Learning for Health (ML4H). :196-208.

Jaiswal A, Li T, Zander C, Han Y, Rousseau JF, Peng Y, Ding Y.  2021.  SCALP - Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata. The IEEE International Conference on Data Mining (ICDM).

Han Y, Chen C, Tang L, Lin M, Jaiswal A, Wang S, Tewfik A, Shih G, Ding Y, Peng Y.  2021.  Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays.. AMIA Annu Symp Proc. 2021:546-555.

Schmeelk S, Dogo MSamuel, Peng Y, Patra BGopal.  2022.  Classifying Cyber-Risky Clinical Notes by Employing Natural Language Processing.. Proc Annu Hawaii Int Conf Syst Sci. 2022:4140-4146.

Tang L, Kooragayalu S, Wang Y, Ding Y, Durrett G, Rousseau JF, Peng Y.  2022.  EchoGen: A New Benchmark Study on Generating Conclusions from Echocardiogram Notes.. Proc Conf Assoc Comput Linguist Meet. 2022:359-368.

Tang L, Kooragayalu S, Wang Y, Ding Y, Durrett G, Rousseau JF, Peng Y.  2022.  EchoGen: A New Benchmark Study on Generating Conclusions from Echocardiogram Notes.. Proc Conf Assoc Comput Linguist Meet. 2022:359-368.

Benda NC, Rogers C, Sharma M, Narain W, Diamond LC, Ancker J, Seier K, Stetson PD, Sulieman L, Armstrong M et al..  2022.  Identifying Nonpatient Authors of Patient Portal Secure Messages in Oncology: A Proof-of-Concept Demonstration of Natural Language Processing Methods.. JCO Clin Cancer Inform. 6:e2200071.