Literature DB >> 33146581

Automated Aneurysm Detection: Emerging from the Shallow End of the Deep Learning Pool.

David F Kallmes1, Bradley J Erickson1.   

Abstract

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Year:  2020        PMID： 33146581      PMCID： PMC7771990          DOI： 10.1148/radiol.2020203853

Source DB:  PubMed          Journal:  Radiology        ISSN： 0033-8419            Impact factor:   11.105


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  10 in total

1.  Convolutional Neural Networks for the Detection and Measurement of Cerebral Aneurysms on Magnetic Resonance Angiography.

Authors:  Joseph N Stember; Peter Chang; Danielle M Stember; Michael Liu; Jack Grinband; Christopher G Filippi; Philip Meyers; Sachin Jambawalikar
Journal:  J Digit Imaging       Date:  2019-10       Impact factor: 4.056

Review 2.  Can computer-aided detection be detrimental to mammographic interpretation?

Authors:  Liane E Philpotts
Journal:  Radiology       Date:  2009-10       Impact factor: 11.105

Review 3.  Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives.

Authors:  Z Shi; B Hu; U J Schoepf; R H Savage; D M Dargis; C W Pan; X L Li; Q Q Ni; G M Lu; L J Zhang
Journal:  AJNR Am J Neuroradiol       Date:  2020-03-12       Impact factor: 3.825

4.  The International Cooperative Study on the Timing of Aneurysm Surgery. Part 1: Overall management results.

Authors:  N F Kassell; J C Torner; E C Haley; J A Jane; H P Adams; G L Kongable
Journal:  J Neurosurg       Date:  1990-07       Impact factor: 5.115

5.  Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography.

Authors:  Takahiro Nakao; Shouhei Hanaoka; Yukihiro Nomura; Issei Sato; Mitsutaka Nemoto; Soichiro Miki; Eriko Maeda; Takeharu Yoshikawa; Naoto Hayashi; Osamu Abe
Journal:  J Magn Reson Imaging       Date:  2017-08-24       Impact factor: 4.813

6.  Automated computerized scheme for detection of unruptured intracranial aneurysms in three-dimensional magnetic resonance angiography.

Authors:  Hidetaka Arimura; Qiang Li; Yukunori Korogi; Toshinori Hirai; Hiroyuki Abe; Yasuyuki Yamashita; Shigehiko Katsuragawa; Ryuji Ikeda; Kunio Doi
Journal:  Acad Radiol       Date:  2004-10       Impact factor: 3.173

7.  Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms.

Authors:  Daiju Ueda; Akira Yamamoto; Masataka Nishimori; Taro Shimono; Satoshi Doishita; Akitoshi Shimazaki; Yutaka Katayama; Shinya Fukumoto; Antoine Choppin; Yuki Shimahara; Yukio Miki
Journal:  Radiology       Date:  2018-10-23       Impact factor: 11.105

8.  Unruptured intracranial aneurysms: natural history, clinical outcome, and risks of surgical and endovascular treatment.

Authors:  David O Wiebers; J P Whisnant; J Huston; I Meissner; R D Brown; D G Piepgras; G S Forbes; K Thielen; D Nichols; W M O'Fallon; J Peacock; L Jaeger; N F Kassell; G L Kongable-Beckman; J C Torner
Journal:  Lancet       Date:  2003-07-12       Impact factor: 79.321

9.  Deep Learning for Detecting Cerebral Aneurysms with CT Angiography.

Authors:  Jiehua Yang; Mingfei Xie; Canpei Hu; Osamah Alwalid; Yongchao Xu; Jia Liu; Teng Jin; Changde Li; Dandan Tu; Xiaowu Liu; Changzheng Zhang; Cixing Li; Xi Long
Journal:  Radiology       Date:  2020-11-03       Impact factor: 11.105

10.  Deep Learning-Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model.

Authors:  Allison Park; Chris Chute; Pranav Rajpurkar; Joe Lou; Robyn L Ball; Katie Shpanskaya; Rashad Jabarkheel; Lily H Kim; Emily McKenna; Joe Tseng; Jason Ni; Fidaa Wishah; Fred Wittber; David S Hong; Thomas J Wilson; Safwan Halabi; Sanjay Basu; Bhavik N Patel; Matthew P Lungren; Andrew Y Ng; Kristen W Yeom
Journal:  JAMA Netw Open       Date:  2019-06-05
  10 in total
  1 in total

1.  Multi-View Convolutional Neural Networks in Rupture Risk Assessment of Small, Unruptured Intracranial Aneurysms.

Authors:  Jun Hyong Ahn; Heung Cheol Kim; Jong Kook Rhim; Jeong Jin Park; Dick Sigmund; Min Chan Park; Jae Hoon Jeong; Jin Pyeong Jeon
Journal:  J Pers Med       Date:  2021-03-24
  1 in total

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