Publications

Kang T, Sun Y, Kim JHyun, Ta C, Perotte A, Schiffer K, Wu M, Zhao Y, Moustafa-Fahmy N, Peng Y et al..  2023.  EvidenceMap: a three-level knowledge representation for medical evidence computation and comprehension.. J Am Med Inform Assoc.

Holste G, Jiang Z, Jaiswal A, Hanna M, Minkowitz S, Legasto AC, Escalon JG, Steinberger S, Bittman M, Shen TC et al..  2023.  How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers? Med Image Comput Comput Assist Interv. 14224:663-673.

Keloth VK, Banda JM, Gurley M, Heider PM, Kennedy G, Liu H, Liu F, Miller T, Natarajan K, Patterson OV et al..  2023.  Representing and Utilizing Clinical Textual Data for Real World Studies: An OHDSI Approach.. J Biomed Inform. :104343.

Moukheiber D, Mahindre S, Moukheiber L, Moukheiber M, Wang S, Ma C, Shih G, Peng Y, Gao M.  2022.  Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays.. Data Augment Label Imperfections (2022). 13567:112-122.

Moukheiber D, Mahindre S, Moukheiber L, Moukheiber M, Wang S, Ma C, Shih G, Peng Y, Gao M.  2022.  Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays.. Data Augment Label Imperfections (2022). 13567:112-122.

Moukheiber D, Mahindre S, Moukheiber L, Moukheiber M, Wang S, Ma C, Shih G, Peng Y, Gao M.  2022.  Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays.. Data Augment Label Imperfections (2022). 13567:112-122.

Moukheiber D, Mahindre S, Moukheiber L, Moukheiber M, Wang S, Ma C, Shih G, Peng Y, Gao M.  2022.  Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays.. Data Augment Label Imperfections (2022). 13567:112-122.

Moukheiber D, Mahindre S, Moukheiber L, Moukheiber M, Wang S, Ma C, Shih G, Peng Y, Gao M.  2022.  Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays.. Data Augment Label Imperfections (2022). 13567:112-122.

Mathai TSudharshan, Lee S, Elton DC, Shen TC, Peng Y, Lu Z, Summers RM.  2022.  Lymph node detection in T2 MRI with transformers. SPIE Medical Imaging. :120333B.

Wanyan T, Lin M, Klang E, Menon KM, Gulamali FF, Azad A, Zhang Y, Ding Y, Wang Z, Wang F et al..  2022.  Supervised Pretraining through Contrastive Categorical Positive Samplings to Improve COVID-19 Mortality Prediction.. ACM BCB. 2022

Wang S, Tang L, Majety A, Rousseau JF, Shih G, Ding Y, Peng Y.  2022.  Trustworthy assertion classification through prompting.. J Biomed Inform. 132:104139.

Mathai TSudharshan, Lee S, Elton DC, Shen TC, Peng Y, Lu Z, Summers RM.  2021.  Detection of lymph nodes in T2 MRI using neural network ensembles. Learning in Medical Imaging (MLMI). :682-691.

Ji Z, Shaikh MAbuzar, Moukheiber D, Srihari SN, Peng Y, Gao M.  2021.  Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment. International Workshop on Machine Learning in Medical Imaging.

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.

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

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.

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

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.