Automated aneurysm detection: Emerging from the shallow end of the deep learning pool

David F. Kallmes; Bradley J. Erickson

doi:10.1148/RADIOL.2020203853

Automated aneurysm detection: Emerging from the shallow end of the deep learning pool

David F. Kallmes, Bradley J. Erickson

Radiology

Research output: Contribution to journal › Editorial › peer-review

2 Scopus citations

Original language	English (US)
Pages (from-to)	164-165
Number of pages	2
Journal	Radiology
Volume	298
Issue number	1
DOIs	https://doi.org/10.1148/RADIOL.2020203853
State	Published - 2021

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging

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10.1148/RADIOL.2020203853

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@article{e563dcb013a84e46abd123dd04046080,

title = "Automated aneurysm detection: Emerging from the shallow end of the deep learning pool",

author = "Kallmes, {David F.} and Erickson, {Bradley J.}",

year = "2021",

doi = "10.1148/RADIOL.2020203853",

language = "English (US)",

volume = "298",

pages = "164--165",

journal = "Radiology",

issn = "0033-8419",

publisher = "Radiological Society of North America Inc.",

number = "1",

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TY - JOUR

T1 - Automated aneurysm detection

T2 - Emerging from the shallow end of the deep learning pool

AU - Kallmes, David F.

AU - Erickson, Bradley J.

PY - 2021

Y1 - 2021

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DO - 10.1148/RADIOL.2020203853

M3 - Editorial

C2 - 33146581

AN - SCOPUS:85099008282

SN - 0033-8419

VL - 298

SP - 164

EP - 165

JO - Radiology

JF - Radiology

IS - 1

ER -

Automated aneurysm detection: Emerging from the shallow end of the deep learning pool

ASJC Scopus subject areas

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