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We describe a deep learning approach for automated brain hemorrhage detection from computed tomography (CT) scans.
“Recurrent Models of Visual Attention,”
Volodymyr Mnih, Nicolas Heess, Alex Graves, and Koray Kavukcuoglu, · 2014
Earlier work this paper cites.
“Multiple Object Recognition with Visual Attention,”
Jimmy Ba, Volodymyr Mnih, and Koray Kavukcuoglu, · 2014
Earlier work this paper cites.
“Show and Tell: A Neural Image Caption Generator,”
Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan, · 2014
Earlier work this paper cites.
“U-Net: Convolutional Networks for Biomedical Image Segmentation,”
Olaf Ronneberger, Philipp Fischer, and Thomas Brox, · 2015
Earlier work this paper cites.
“ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering,”
Kan Chen, Jiang Wang, Liang-Chieh Chen, Haoyuan Gao, Wei Xu, and Ram Nevatia, · 2015
Earlier work this paper cites.
“Attention to Scale: Scale-aware Semantic Image Segmentation,”
Liang-Chieh Chen, Yi Yang, Jiang Wang, Wei Xu, and Alan L. Yuille, · 2015
Earlier work this paper cites.
“Long-term recurrent convolutional networks for visual recognition and description,”
Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell, · 2015
Earlier work this paper cites.
“Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2015
Earlier work this paper cites.
“Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,”
Varun Gulshan, Lily Peng, Marc Coram, Martin C. Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, Ramasamy Kim, Rajiv Raman, Philip C. Nelson, Jessica L. Mega, and Dale R. Webster, · 2016
Cited alongside, same era.
“Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images,”
M. J. J. P. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, · 2016
Cited alongside, same era.
“Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer,”
Sergey Zagoruyko and Nikos Komodakis, · 2016
Cited alongside, same era.
“Recurrent Convolutional Networks for Pulmonary Nodule Detection in CT Imaging,”
P.-P. Ypsilantis and G. Montana, · 2016
Cited alongside, same era.
“Combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation,”
“Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection,”
Zhe Wang, Yanxin Yin, Jianping Shi, Wei Fang, Hongsheng Li, and Xiaogang Wang, · 2017
Closest in time.
“VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images,”
Hao Chen, Qi Dou, Lequan Yu, Jing Qin, and Pheng-Ann Heng, · 2017
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“Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks,”
Pranav Rajpurkar, Awni Y. Hannun, Masoumeh Haghpanahi, Codie Bourn, and Andrew Y. Ng, · 2017
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“Superhuman Accuracy on the SNEMI3D Connectomics Challenge,”
Kisuk Lee, Jonathan Zung, Peter Li, Viren Jain, and H. Sebastian Seung, · 2017
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“FastVentricle: Cardiac Segmentation with ENet,”
Jesse Lieman-Sifry, Matthieu Lê, Felix Lau, Sean Sall, and Daniel Golden, · 2017
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Jianxu Chen, Lin Yang, Yizhe Zhang, Mark Alber, and Danny Z Chen, · 2016
Cited alongside, same era.
“Densely connected convolutional networks,”
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger, · 2017
Cited alongside, same era.
“Classification of CT brain images based on deep learning networks,”
Xiaohong W. Gao, Rui Hui, and Zengmin Tian, · 2017
Cited alongside, same era.
“Skeleton-based Action Recognition with Convolutional Neural Networks,”
Chao Li, Qiaoyong Zhong, Di Xie, and Shiliang Pu, · 2017
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Jinzheng Cai, Le Lu, Yuanpu Xie, Fuyong Xing, and Lin Yang, · 2017
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