Publications citing MIMIC-CXR Database v2.1.0

Deep medical cross-modal attention hashing
Zhang Yong, Weihua Ou, Yufeng Shi et al. World Wide Web, 2021. Cites: paper

Explaining the Black-box Smoothly- A Counterfactual Approach
Sumedha Singla, Motahhare Eslami, Brian Pollack et al. Medical Image Analysis, 2021. Cites: paper

Weighing features of lung and heart regions for thoracic disease classification
Jiansheng Fang, Yanwu Xu, Yitian Zhao et al. BMC Medical Imaging, 2021. Cites: paper

Deep learning for chest X-ray analysis: A survey
Erdi Çallı, Ecem Sogancioglu, Bram van Ginneken et al. Medical Image Analysis, 2021. Cites: paper

Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs
Bradley Segal, David M. Rubin, Grace Rubin et al. SN Computer Science, 2021. Cites: paper

Tips and Tricks to Improve CNN-based Chest X-ray Diagnosis: A Survey
Changhee Han, Takayuki Okamoto, Takeuchi, Koichi et al. arXiv (Cornell University), 2021. Cites: paper

Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest Images
Mehdi Cherti, Jenia Jitsev. arXiv (Cornell University), 2021. Cites: paper

Identification and Localization of Endotracheal Tube on Chest Radiographs Using a Cascaded Convolutional Neural Network Approach
Su Kara, Jake Y. Akers, Peter Chang. Journal of Imaging Informatics in Medicine, 2021. Cites: dataset, paper

Detection and Semiquantitative Analysis of Cardiomegaly, Pneumothorax, and Pleural Effusion on Chest Radiographs
Leilei Zhou, Xindao Yin, Tao Zhang et al. Radiology Artificial Intelligence, 2021. Cites: paper

Human Uncertainty Inference via Deterministic Ensemble Neural Networks
Yujin Cha, Sang Wan Lee. Proceedings of the AAAI Conference on Artificial Intelligence, 2021. Cites: dataset, paper

Searching for pneumothorax in x-ray images using autoencoded deep features
Antonio Sze-To, Abtin Riasatian, Hamid R. Tizhoosh. Scientific Reports, 2021. Cites: paper

Chest x-ray automated triage: A semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures
Candelaria Mosquera, Facundo Nahuel Díaz, Fernando Binder et al. Computer Methods and Programs in Biomedicine, 2021. Cites: paper

Deep learning to detect acute respiratory distress syndrome on chest radiographs: a retrospective study with external validation
Michael W. Sjoding, Daniel Taylor, Jonathan Motyka et al. The Lancet Digital Health, 2021. Cites: paper

Rethinking annotation granularity for overcoming deep shortcut learning: A retrospective study on chest radiographs.
Luyang Luo, Hao Chen, Yongjie Xiao et al. arXiv (Cornell University), 2021. Cites: paper

Data valuation for medical imaging using Shapley value and application to a large-scale chest X-ray dataset
Siyi Tang, Amirata Ghorbani, Rikiya Yamashita et al. Scientific Reports, 2021. Cites: paper

Evaluating diagnostic content of AI-generated radiology reports of chest X-rays
Zaheer Babar, Twan van Laarhoven, Fabio Massimo Zanzotto et al. Artificial Intelligence in Medicine, 2021. Cites: paper

A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images
Guangyu Wang, Xiaohong Liu, Jun Shen et al. Nature Biomedical Engineering, 2021. Cites: paper

Deep Learning Systems for Pneumothorax Detection on Chest Radiographs: A Multicenter External Validation Study
Yee Liang Thian, Dianwen Ng, James Thomas Patrick Decourcy Hallinan et al. Radiology Artificial Intelligence, 2021. Cites: paper

MVC-NET: Multi-View Chest Radiograph Classification Network With Deep Fusion
Xiongfeng Zhu, Qianjin Feng. IEEE International Symposium on Biomedical Imaging (ISBI), 2021. Cites: paper

A clinical validation of VinDr-CXR, an AI system for detecting abnormal chest radiographs
Ngoc Huy Nguyen, Ha Q. Nguyen, Nghia T. Nguyen et al. arXiv (Cornell University), 2021. Cites: paper

Show, tell and summarise: learning to generate and summarise radiology findings from medical images
Sonit Singh, Sarvnaz Karimi, Kevin Ho‐Shon et al. Neural Computing and Applications, 2021. Cites: paper

How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals
Eric Wu, Kevin Wu, Roxana Daneshjou et al. Nature Medicine, 2021. Cites: paper

Putting the Pieces Together: Deep Learning for Knee MRI Multitissue Abnormality Detection and Severity Grading
Matthew Li, Connie Y. Chang. Radiology Artificial Intelligence, 2021. Cites: paper

Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays
Xiaosong Wang, Ziyue Xu, Leo Tam et al. arXiv (Cornell University), 2021. Cites: paper

Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development
Alexandros Karargyris, Satyananda Kashyap, Ismini Lourentzou et al. Scientific Data, 2021. Cites: paper

CheXternal
Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek et al. ACM Conference on Health, Inference, and Learning (CHIL), 2021. Cites: paper

Analysis of Potential for User Errors in Mobile Deployment of Radiology Deep Learning for Cardiac Rhythm Device Detection
Carl F. Sabottke, Marc A. Breaux, Rebecca Lee et al. Journal of Imaging Informatics in Medicine, 2021. Cites: paper

Review of Artificial Intelligence Training Tools and Courses for Radiologists
Michael L. Richardson, Scott Adams, Atul Kumar Agarwal et al. Academic Radiology, 2021. Cites: paper

Artificial Intelligence to Diagnose Heart Failure Based on Chest X-Rays and Potential Clinical Implications
Scott Adams, Haissam A. Haddad. Canadian Journal of Cardiology, 2021. Cites: paper

RTEX: A novel framework for ranking, tagging, and explanatory diagnostic captioning of radiography exams
Vasiliki Kougia, John Pavlopoulos, Panagiotis Papapetrou et al. Journal of the American Medical Informatics Association, 2021. Cites: paper

Quantifying Pulmonary Edema on Chest Radiographs
William F. Auffermann. Radiology Artificial Intelligence, 2021. Cites: paper

Synthesis of COVID-19 chest X-rays using unpaired image-to-image translation
Hasib Zunair, A. Ben Hamza. Social Network Analysis and Mining, 2021. Cites: paper

Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort
Eun Young Kim, Young Jae Kim, Won-Jun Choi et al. PLoS ONE, 2021. Cites: paper

Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph
Po‐Chih Kuo, Cheng Che Tsai, Diego M. López et al. npj Digital Medicine, 2021. Cites: paper

Deep triplet hashing network for case-based medical image retrieval
Jiansheng Fang, Huazhu Fu, Jiang Liu. Medical Image Analysis, 2021. Cites: dataset

Automatic medical image interpretation: State of the art and future directions
Hareem Ayesha, Sajid Iqbal, Mehreen Tariq et al. Pattern Recognition, 2021. Cites: paper

Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning
Matthew Watson, Noura Al Moubayed. Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition, 2021. Cites: paper

Automatic clustering method to segment COVID-19 CT images
Mohamed Abd Elaziz, Mohammed A. A. Al‐qaness, Esraa Osama Abo Zaid et al. PLoS ONE, 2021. Cites: paper

Deep learning-enabled medical computer vision
Andre Esteva, Katherine Chou, Serena Y. Yeung et al. npj Digital Medicine, 2021. Cites: paper

Deep Learning to Quantify Pulmonary Edema in Chest Radiographs
Steven Horng, Ruizhi Liao, Xin Wang et al. Radiology Artificial Intelligence, 2021. Cites: paper

AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation
Di You, Fenglin Liu, Shen Ge et al. Lecture notes in computer science, 2021. Cites: paper

A multi-BERT hybrid system for Named Entity Recognition in Spanish radiology reports.
Víctor Suárez-Paniagua, Hang Dong, Arlene Casey. Edinburgh Research Explorer (University of Edinburgh), 2021. Cites: paper

AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-Ray
Nkechinyere N. Agu, Joy Tzung-Yu Wu, Hanqing Chao et al. Lecture notes in computer science, 2021. Cites: paper

Automated Generation of Accurate & Fluent Medical X-ray Reports
Hoang Nga Nguyen, Dong Nie, Taivanbat Badamdorj et al. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021. Cites: paper

BDKG at MEDIQA 2021: System Report for the Radiology Report Summarization Task
Songtai Dai, Quan Wang, Yajuan Lyu et al. Workshop on Biomedical Language Processing (BioNLP), 2021. Cites: paper

Clinically Correct Report Generation from Chest X-Rays Using Templates
Pablo Pino, Denis Parra, Cecilia Besa et al. Lecture notes in computer science, 2021. Cites: paper

Coherent and Concise Radiology Report Generation via Context Specific Image Representations and Orthogonal Sentence States
Litton J Kurisinkel, Ai Ti Aw, Nancy F. Chen. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), 2021. Cites: paper

Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare
Manish Gawali, C. S. Arvind, Shriya Suryavanshi et al. Lecture notes in computer science, 2021. Cites: paper

Deep Learning Classification of Cardiomegaly Using Combined Imaging and Non-imaging ICU Data
Declan Grant, Bartłomiej W. Papież, Guy Parsons et al. Lecture notes in computer science, 2021. Cites: paper

Deep Learning to Quantify Pulmonary Edema in Chest Radiographs
Steven Horng, Ruizhi Liao, Xin Wang et al. DSpace@MIT (Massachusetts Institute of Technology), 2021. Cites: paper

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