A Computer-Aided Detection to Intracranial Hemorrhage by Using Deep Learning: A Case Study

[1]  Lipo Wang,et al.  Deep Learning Applications in Medical Image Analysis , 2018, IEEE Access.

[2]  Winston Chong,et al.  The ASPECTS template is weighted in favor of the striatocapsular region , 2006, NeuroImage.

[3]  Václav Snásel,et al.  Brain Hemorrhage Diagnosis by Using Deep Learning , 2017, ICMLSC.

[4]  Nicholas G. Polson,et al.  Deep learning for short-term traffic flow prediction , 2016, 1604.04527.

[5]  Yang Liu,et al.  Maternal DCAF13 Regulates Chromatin Tightness to Contribute to Embryonic Development , 2019, Scientific Reports.

[6]  Nan Wu,et al.  Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening , 2019, IEEE Transactions on Medical Imaging.

[7]  F. Murphy,et al.  Claustrophobia in magnetic resonance imaging: A systematic review and meta-analysis , 2015 .

[8]  Qi Tian,et al.  Classification of CT Brain Images of Head Trauma , 2007, PRIB.

[9]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[10]  Navalgund Rao,et al.  Deep 3D convolution neural network for CT brain hemorrhage classification , 2018, Medical Imaging.

[11]  J. Sivaswamy,et al.  A method for automatic detection and classification of stroke from brain CT images , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Kostas Marias,et al.  Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification With Application to MRI Liver Tumor Differentiation , 2019, IEEE Journal of Biomedical and Health Informatics.

[13]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[14]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[15]  Li Shen,et al.  Deep Learning to Improve Breast Cancer Detection on Screening Mammography , 2017, Scientific Reports.

[16]  Aldenor G. Santos,et al.  Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles , 2019, Scientific Reports.

[17]  Celina Imielinska,et al.  Toward objective quantification of perfusion-weighted computed tomography in subarachnoid hemorrhage: quantification of symmetry and automated delineation of vascular territories. , 2005, Academic radiology.

[18]  SchmidhuberJürgen.  Deep learning in neural networks , 2015 .

[19]  Tao Chan,et al.  Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain , 2007, Comput. Medical Imaging Graph..

[20]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[22]  Toshinori Hirai,et al.  Detection of hemorrhagic hypointense foci in the brain on susceptibility-weighted imaging clinical and phantom studies. , 2007, Academic radiology.

[23]  Mohammad Mansouri,et al.  An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets , 2018, Nature Biomedical Engineering.

[24]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[25]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[26]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).