[1] Macdonald RL,
Schweizer TA.
Spontaneous subarachnoid haemorrhage (2017).
Lancet.11;389(10069):655-666.
[2] Nieuwkamp DJ,
Setz LE,
Algra A,
Linn FH,
de Rooij NK,
Rinkel GJ (2009).
Changes in case fatality of aneurysmal subarachnoid haemorrhage over time,
according to age,
sex,
and region: a meta-analysis.
Lancet Neurol.
8(7):635-42.
[3] Lawton MT,
Vates GE (2017).
Subarachnoid Hemorrhage.
N Engl J Med.
377(3):257-266.
[4] Singer RJ,
Ogilvy CS,
Rordorf G.
Aneurysmal subarachnoid hemorrhage: Epidemiology,
risk factors,
and pathogenesis.
In: Biller J,
ed.
UpToDate 2018.
[5] Frontera J,
Claassen J,
Schmidt JM,
et al.
(2006) Prediction of symptomatic vasospasm after subarachnoid hemorrhage: the modified Fisher scale.
Neurosurgery.
59: 21–27.
[6] Lee H.,
Yune S.,
Mansouri M.,
et al.
(2018) An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets.
Nature Biomedical Engineering.
DOI: 10.1038/s41551-018-0324-9
[7] Szegedy,
C.,
Ioffe,
S.,
Vanhoucke,
V.
& Alemi,
A.
A.
(2017) Inception-v4,
inception-ResNet and the impact of residual connections on learning.
In Proc.
31st AAAI Conference on Artificial Intelligence 4278–4284.
[8] DeLong ER,
DeLong DM,
Clarke-Pearson DL.
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.
Biometrics.
1988 Sep;44(3):837-45.
[9] Selvaraju RS,
Cogswell M,
Das A,
Vedantam R,
Parikh D,
Batra D.
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization.
Retrieved from https://arxiv.org/abs/1610.02391