| 1 |
Ikram MA, Wieberdink RG, Koudstaal PJ. International epidemiology of intracerebral hemorrhage [J]. Curr Atheroscler Rep, 2012, 14(4): 300-306.
|
| 2 |
曹勇, 张谦, 于洮, 等. 中国脑血管病临床管理指南（节选版）——脑出血临床管理 [J]. 中国卒中杂志, 2019, 14(8): 809-813.
|
| 3 |
王陇德, 彭斌, 张鸿祺, 等. 《中国脑卒中防治报告2020》概要 [J]. 中国脑血管病杂志, 2022, 19(2): 136-144.
|
| 4 |
Ye H, Gao F, Yin Y, et al. Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network [J]. Eur Radiol, 2019, 29(11): 6191-6201.
|
| 5 |
Kothari RU, Brott T, Broderick JP, et al. The ABCs of measuring intracerebral hemorrhage volumes [J]. Stroke, 1996, 27(8): 1304-1305.
|
| 6 |
Poon MT, Fonville AF, Al-Shahi Salman R. Long-term prognosis after intracerebral haemorrhage: systematic review and meta-analysis [J]. J Neurol Neurosurg Psychiatry, 2014, 85(6): 660-667.
|
| 7 |
Morotti A, Li Q, Mazzoleni V, et al. Non-contrast CT markers of intracerebral hemorrhage expansion: the influence of onset-to-CT time [J]. Int J Stroke, 2022: 17474930221142742.
|
| 8 |
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence [J]. Nat Med, 2019, 25(1): 44-56.
|
| 9 |
Voter AF, Meram E, Garrett JW, et al. Diagnostic accuracy and failure mode analysis of a deep learning algorithm for the detection of intracranial hemorrhage [J]. J Am Coll Radiol, 2021, 18(8): 1143-1152.
|
| 10 |
Lee JY, Kim JS, Kim TY, et al. Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm [J]. Sci Rep, 2020, 10(1): 20546.
|
| 11 |
Majumdar A, Brattain L, Telfer B, et al. Detecting intracranial hemorrhage with deep learning [J]. Annu Int Conf IEEE Eng Med Biol Soc, 2018, 2018: 583-587.
|
| 12 |
Zhao K, Zhao Q, Zhou P, et al. Can artificial intelligence be applied to diagnose intracerebral hemorrhage under the background of the fourth industrial revolution? A novel systemic review and meta-analysis [J]. Int J Clin Pract, 2022, 2022: 9430097.
|
| 13 |
Mclouth J, Elstrott S, Chaibi Y, et al. Validation of a deep learning tool in the detection of intracranial hemorrhage and large vessel occlusion [J]. Front Neurol, 2021, 12: 656112.
|
| 14 |
Chang PD, Kuoy E, Grinband J, et al. Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT [J]. AJNR Am J Neuroradiol, 2018, 39(9): 1609-1616.
|
| 15 |
Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, et al. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration [J]. NPJ Digit Med, 2018, 1: 9.
|
| 16 |
Cho J, Park KS, Karki M, et al. Improving sensitivity on identification and delineation of intracranial hemorrhage lesion using cascaded deep learning models [J]. J Digit Imaging, 2019, 32(3): 450-461.
|
| 17 |
Alis D, Alis C, Yergin M, et al. A joint convolutional-recurrent neural network with an attention mechanism for detecting intracranial hemorrhage on noncontrast head CT [J]. Sci Rep, 2022, 12(1): 2084.
|
| 18 |
Wang X, Shen T, Yang S, et al. A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans [J]. Neuroimage Clin, 2021, 32: 102785.
|
| 19 |
周长才, 刘爽, 王昕. 基于卷积神经网络的颅内出血检测 [J]. 长春工业大学学报, 2021, 42(5): 469-473.
|
| 20 |
李娟, 汤翔宇, 沈逸, 等. 基于卷积神经网络的深度学习算法对颅内出血的类型识别及血肿分割一致性的研究 [J]. 放射学实践, 2021, 36(1): 7-12.
|
| 21 |
Ironside N, Chen CJ, Mutasa S, et al. Fully automated segmentation algorithm for hematoma volumetric analysis in spontaneous intracerebral hemorrhage [J]. Stroke, 2019, 50(12): 3416-3423.
|
| 22 |
Dhar R, Falcone GJ, Chen Y, et al. Deep learning for automated measurement of hemorrhage and perihematomal edema in supratentorial intracerebral hemorrhage [J]. Stroke, 2020, 51(2): 648-651.
|
| 23 |
苗政, 李明洋, 陈忠萍, 等. 基于深度学习分割模型的脑出血CT图像自动分割研究 [J]. 中国医疗设备, 2022, 37(8): 46-50+86.
|
| 24 |
Kellogg RT, Vargas J, Barros G, et al. Segmentation of chronic subdural hematomas using 3D convolutional neural networks [J]. World Neurosurg, 2021, 148: e58-e65.
|
| 25 |
Gou X, He X. Deep Learning-based detection and diagnosis of subarachnoid hemorrhage [J]. J Healthc Eng, 2021, 2021: 9639419.
|
| 26 |
Zhao X, Chen K, Wu G, et al. Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema [J]. Eur Radiol, 2021, 31(7): 5012-5020.
|
| 27 |
Peng Q, Chen X, Zhang C, et al. Deep learning-based computed tomography image segmentation and volume measurement of intracerebral hemorrhage [J]. Front Neurosci, 2022, 16: 965680.
|
| 28 |
Xu J, Zhang R, Zhou Z, et al. Deep network for the automatic segmentation and quantification of intracranial hemorrhage on CT [J]. Front Neurosci, 2020, 14: 541817.
|
| 29 |
Yu N, Yu H, Li H, et al. A Robust deep learning segmentation method for hematoma volumetric detection in intracerebral hemorrhage [J]. Stroke, 2022, 53(1): 167-176.
|
| 30 |
Rava RA, Seymour SE, Laque ME, et al. Assessment of an artificial intelligence algorithm for detection of intracranial hemorrhage [J]. World Neurosurg, 2021, 150: e209-e217.
|
| 31 |
Kuang Z, Deng X, Yu L, et al. Ψ-Net: Focusing on the border areas of intracerebral hemorrhage on CT images [J]. Comput Methods Programs Biomed, 2020, 194: 105546.
|