Crowd-Sourced Deep Learning for Intracranial Hemorrhage Identification: Wisdom of Crowds or Laissez-Faire

References

1. 1.↵
   1. Korot E,
   2. Guan Z,
   3. Ferraz D, et al

   . Code-free deep learning for multi-modality medical image classification. Nat Mach Intell 2021;3:288–98 doi:10.1038/s42256-021-00305-2

   CrossRef
2. 2.↵
   1. Wang T,
   2. Song N,
   3. Liu L, et al

   . Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement. BMC Med Imaging 2021;21:125 doi:10.1186/s12880-021-00657-6 pmid:34388981

   CrossRefPubMed
3. 3.↵
   1. Sharrock MF,
   2. Mould WA,
   3. Ali H, et al

   . 3D deep neural network segmentation of intracerebral hemorrhage: development and validation for clinical trials. Neuroinformatics 2021;19:403–15 doi:10.1007/s12021-020-09493-5 pmid:32980970

   CrossRefPubMed
4. 4.↵
   1. Teng L,
   2. Ren Q,
   3. Zhang P, et al

   . Artificial intelligence can effectively predict early hematoma expansion of intracerebral hemorrhage analyzing noncontrast computed tomography image. Front Aging Neurosci 2021;13:632138 doi:10.3389/fnagi.2021.632138 pmid:34122038

   CrossRefPubMed
5. 5.↵
   1. Dhar R,
   2. Falcone GJ,
   3. Chen Y, et al

   . Deep learning for automated measurement of hemorrhage and perihematomal edema in supratentorial intracerebral hemorrhage. Stroke 2020;51:648–51 doi:10.1161/STROKEAHA.119.027657 pmid:31805845

   CrossRefPubMed
6. 6.↵
   1. Hastie T,
   2. Tibshirani R,
   3. Friedman J

   1. Hastie T,
   2. Tibshirani R,
   3. Friedman J

   . Boosting and Additive Trees. In: Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag; 2009:337–87
7. 7.↵
   1. Hastie T,
   2. Tibshirani R,
   3. Friedman J

   1. Hastie T,
   2. Tibshirani R,
   3. Friedman J

   . Model Inference and Averaging. In: Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag; 2009:261–94
8. 8.↵
   1. Kittler J,
   2. Roli F

   1. Dietterich TG

   . Ensemble Methods in Machine Learning. In: Kittler J, Roli F, eds. Multiple Classifier Systems. Vol 12. Springer-Verlag; 2000;1857:1–15
9. 9.↵
   1. Zhou ZH,
   2. Jiang Y,
   3. Yang YB, et al

   . Lung cancer cell identification based on artificial neural network ensembles. Artif Intell Med 2002;24:25–36 doi:10.1016/s0933-3657(01)00094-x pmid:11779683

   CrossRefPubMed
10. 10.↵
    1. Jerebko AK,
    2. Malley JD,
    3. Franaszek M, et al

    . Support vector machines committee classification method for computer-aided polyp detection in CT colonography. Acad Radiol 2005;12:479–86 doi:10.1016/j.acra.2004.04.024 pmid:15831422

    CrossRefPubMed
11. 11.↵
    1. Ochs R,
    2. Goldin J,
    3. Abtin F, et al

    . Automated classification of lung bronchovascular anatomy in CT using AdaBoost. Med Image Anal 2007;11:315–24 doi:10.1016/j.media.2007.03.004 pmid:17482500

    CrossRefPubMed
12. 12.↵
    1. Mougiakakou SG,
    2. Valavanis IK,
    3. Nikita A, et al

    . Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers. Artif Intell Med 2007;41:25–37 doi:10.1016/j.artmed.2007.05.002 pmid:17624744

    CrossRefPubMed
13. 13.↵
    1. Abujudeh HH

    . Emergency Radiology. Oxford University Press; 2016
14. 14.↵
    1. Swain PH,
    2. Hauska H

    . The decision tree classifier: design and potential. IEEE Transactions on Geoscience Electronics 1977;15:142–47 doi:10.1109/TGE.1977.6498972

    CrossRef
15. 15.↵
    1. Vapnik VN

    . The Nature of Statistical Learning Theory. 2nd ed. Springer-Verlag; 2000
16. 16.↵
    1. Ding C,
    2. Peng H

    . Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 2005;3:185–205 doi:10.1142/s0219720005001004 pmid:15852500

    CrossRefPubMed
17. 17.↵
    1. DeLong ER,
    2. DeLong DM,
    3. Clarke-Pearson DL

    . Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837–45 doi:10.2307/2531595 pmid:3203132

    CrossRefPubMedWeb of Science
18. 18.↵
    1. Andrews CM,
    2. Jauch EC,
    3. Hemphill JC, et al

    . Emergency neurological life support: intracerebral hemorrhage. Neurocrit Care 2012;17(Suppl 1):37–46 doi:10.1007/s12028-012-9757-2 pmid:22965322

    CrossRefPubMed
19. 19.↵
    1. Shahin AH,
    2. Kamal A,
    3. Elattar MA

    . Deep Ensemble Learning for Skin Lesion Classification from Dermoscopic Images. In: Proceedings of the 9th Cairo International Biomedical Engineering Conference (CIBEC), Cairo, Egypt. December 20–22, 2018 doi:10.1109/CIBEC.2018.8641815

    CrossRef
20. 20.↵
    1. Rajaraman S,
    2. Sornapudi S,
    3. Alderson PO, et al

    . Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs. PLoS One 2020;15:e0242301 doi:10.1371/journal.pone.0242301 pmid:33180877

    CrossRefPubMed
21. 21.↵
    1. Zabihollahy F,
    2. Ukwatta E,
    3. Krishna S, et al

    . Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method. J Magn Reson Imaging 2020;51:1223–34 doi:10.1002/jmri.26913 pmid:31456317

    CrossRefPubMed