Augmenting the National Institutes of Health Chest Radiograph Dataset with Expert Annotations of Possible Pneumonia.
George Shih,Carol C. Wu,Safwan Halabi,Marc D. Kohli,Luciano M. Prevedello,Tessa S. Cook,Arjun Sharma,Judith K. Amorosa,Veronica Arteaga,Maya Galperin-Aizenberg,Ritu R. Gill,Myrna C. B. Godoy,Stephen B. Hobbs,Jean Jeudy,Archana T Laroia,Palmi Shah,Dharshan Vummidi,Kavitha Yaddanapudi,Kavitha Yaddanapudi,Anouk Stein +19 more
- Vol. 1, Iss: 1
TLDR
This dataset is intended to be used for machine learning and is composed of annotations with bounding boxes for pulmonary opacity on chest radiographs which may represent pneumonia in the appropriate clinical setting.Abstract:
This dataset is intended to be used for machine learning and is composed of annotations with bounding boxes for pulmonary opacity on chest radiographs which may represent pneumonia in the appropria...read more
Citations
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Journal ArticleDOI
Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays
Sivaramakrishnan Rajaraman,Jenifer Siegelman,Philip O. Alderson,Lucas S. Folio,Les R. Folio,Sameer Antani +5 more
TL;DR: Use of iteratively pruned deep learning model ensembles for detecting pulmonary manifestations of COVID-19 with chest X-rays and the combined use of modality-specific knowledge transfer, iterative model pruning, and ensemble learning resulted in improved predictions.
Journal Article
Contrastive Learning of Medical Visual Representations from Paired Images and Text
TL;DR: This work proposes an alternative unsupervised strategy to learn medical visual representations directly from the naturally occurring pairing of images and textual data, and shows that this method leads to image representations that considerably outperform strong baselines in most settings.
Journal ArticleDOI
Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning
Joseph Paul Cohen,Lan Dao,Karsten Roth,Paul Morrison,Yoshua Bengio,Almas F. Abbasi,Beiyi Shen,Hoshmand Kochi Mahsa,Marzyeh Ghassemi,Haifang Li,Timothy Q. Duong +10 more
TL;DR: The results indicate that the model’s ability to gauge the severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU.
Journal ArticleDOI
Automated abnormality classification of chest radiographs using deep convolutional neural networks
Yuxing Tang,Youbao Tang,Yifan Peng,Ke Yan,Mohammadhadi Bagheri,Bernadette Redd,Catherine Brandon,Zhiyong Lu,Mei Han,Jing Xiao,Ronald M. Summers +10 more
TL;DR: The remarkable performance in diagnostic accuracy observed in this study shows that deep CNNs can accurately and effectively differentiate normal and abnormal chest radiographs, thereby providing potential benefits to radiology workflow and patient care.
Journal ArticleDOI
Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions
Luciano M. Prevedello,Safwan Halabi,George Shih,Carol C. Wu,Marc D. Kohli,Falgun H. Chokshi,Bradley J. Erickson,Jayashree Kalpathy-Cramer,Katherine P. Andriole,Adam E. Flanders +9 more
TL;DR: Some of the challenges and potential solutions to advance the field forward in medical imaging are reviewed, with focus on the experience gained by hosting image-based competitions.
References
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Journal ArticleDOI
The Pascal Visual Object Classes (VOC) Challenge
TL;DR: The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse.
Journal ArticleDOI
Fleischner Society: Glossary of Terms for Thoracic Imaging
David M. Hansell,Alexander A. Bankier,Heber MacMahon,Theresa C. McLoud,Nestor L. Müller,J Remy +5 more
TL;DR: Members of the Fleischner Society compiled a glossary of terms for thoracic imaging that replaces previous glossaries published in 1984 and 1996 for Thoracic radiography and computed tomography, respectively.
Journal ArticleDOI
Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
Pranav Rajpurkar,Jeremy Irvin,Robyn L. Ball,Kaylie Zhu,Brandon Yang,Hershel Mehta,Tony Duan,Daisy Ding,Aarti Bagul,Curtis P. Langlotz,Bhavik N. Patel,Kristen W. Yeom,Katie Shpanskaya,Francis G. Blankenberg,Jayne Seekins,Timothy J. Amrhein,David A. Mong,Safwan Halabi,Evan J. Zucker,Andrew Y. Ng,Matthew P. Lungren +20 more
TL;DR: CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs, achieved radiologist-level performance on 11 pathologies and did not achieve radiologists' level performance on 3 pathologies.
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