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Other literature type . 2021
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The RSNA Pulmonary Embolism CT Dataset

Authors: Errol Colak; Felipe Kitamura; Stephen B Hobbs; Carol C Wu; Matthew P. Lungren; Luciano M. Prevedello; Jayashree Kalpathy-Cramer; +23 Authors

The RSNA Pulmonary Embolism CT Dataset

Abstract

This dataset is composed of CT pulmonary angiograms and annotations related to pulmonary embolism. It is available at https://www.rsna.org/education/ai-resources-and-training/ai-image-challenge/rsn...

Country
Belgium
Subjects by Vocabulary

Microsoft Academic Graph classification: medicine.medical_specialty business.industry MEDLINE medicine.disease Pulmonary embolism Text mining medicine Radiology business

Medical Subject Headings: cardiovascular diseases

Keywords

Radiological and Ultrasound Technology, Data Resources, Artificial Intelligence, Radiology, Nuclear Medicine and imaging

12 references, page 1 of 2

1. Goldhaber SZ, Bounameaux H. Pulmonary embolism and deep vein thrombosis. Lancet 2012;379(9828):1835-1846. [OpenAIRE]

2. Becattini C, Agnelli G. Risk straticfiation and management of acute pulmonary embolism. Hematology (Am Soc Hematol Educ Program) 2016;2016(1):404-412. [OpenAIRE]

3. Weiss CR, Scatarige JC, Diette GB, Haponik EF, Merriman B, Fishman EK. CT pulmonary angiography is the rfist-line imaging test for acute pulmonary embolism: a survey of US clinicians. Acad Radiol 2006;13(4):434-446.

4. Lee J, Kirschner J, Pawa S, Wiener DE, Newman DH, Shah K. Computed tomography use in the adult emergency department of an academic urban hospital from 2001 to 2007. Ann Emerg Med 2010;56(6):591-596 [Published correction appears in Ann Emerg Med 2011;57(3):256.].

5. Donohoo JH, Mayo-Smith WW, Pezzullo JA, Egglin TK. Utilization patterns and diagnostic yield of 3421 consecutive multidetector row computed tomography pulmonary angiograms in a busy emergency department. J Comput Assist Tomogr 2008;32(3):421-425.

6. Jha S. Value of triage by articfiial intelligence. Acad Radiol 2020;27(1):153- 155.

7. Remy-Jardin M, Faivre JB, Kaergel R, et al. Machine learning and deep neural network applications in the thorax: pulmonary embolism, chronic thromboembolic pulmonary hypertension, aorta, and chronic obstructive pulmonary disease. J oThrac Imaging 2020;35(Suppl 1):S40-S48.

8. Halabi SS, Prevedello LM, Kalpathy-Cramer J, et al. eTh RSNA pediatric bone age machine learning challenge. Radiology 2019;290(2):498-503.

9. Shih G, Wu CC, Halabi SS, et al. Augmenting the National Institutes of Health chest radiograph dataset with expert annotations of possible pneumonia. Radiol Artif Intell 2019;1(1):e180041.

10. Flanders AE, Prevedello LM, Shih G, et al. Construction of a Machine Learning Dataset through Collaboration: eTh RSNA 2019 Brain CT Hemorrhage Challenge. Radiol Artif Intell 2020;2(3):e190211.

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    40
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
40
Top 1%
Top 10%
Top 1%
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