Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
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Lin Yang | Danny Ziyi Chen | Yizhe Zhang | Jianxu Chen | Siyuan Zhang | D. Chen | L. Yang | Yizhe Zhang | Siyuan Zhang | Jianxu Chen
[1] Yang Li,et al. Gland Instance Segmentation Using Deep Multichannel Neural Networks , 2016, IEEE Transactions on Biomedical Engineering.
[2] Lin Yang,et al. Coarse-to-Fine Stacked Fully Convolutional Nets for lymph node segmentation in ultrasound images , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[3] Joachim M. Buhmann,et al. Crowdsourcing the creation of image segmentation algorithms for connectomics , 2015, Front. Neuroanat..
[4] D. Hochbaum. Approximating covering and packing problems: set cover, vertex cover, independent set, and related problems , 1996 .
[5] Burr Settles,et al. Active Learning Literature Survey , 2009 .
[6] Hao Chen,et al. Gland segmentation in colon histology images: The glas challenge contest , 2016, Medical Image Anal..
[7] Robert Tibshirani,et al. An Introduction to the Bootstrap , 1994 .
[8] Yang Li,et al. Gland Instance Segmentation by Deep Multichannel Side Supervision , 2016, MICCAI.
[9] Hao Chen,et al. Deep Contextual Networks for Neuronal Structure Segmentation , 2016, AAAI.
[10] Hao Chen,et al. DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Kristen Grauman,et al. Active Image Segmentation Propagation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[13] Seunghoon Hong,et al. Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation , 2015, NIPS.
[14] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[15] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).