Tree-based Ensemble Classifier Learning for Automatic Brain Glioma Segmentation
Tài liệu tham khảo
Havaei, 2017, Brain tumor segmentation with deep neural networks, Medical image analysis, 35, 18, 10.1016/j.media.2016.05.004
Valverde, 2017, Improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach, arXiv preprint arXiv:1702.04869
Christ, 2017, Automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks, arXiv preprint arXiv:1702.05970
Folgoc, 2016, Segmentation of brain tumors via cascades of lifted decision forests, Proceedings of MICCAI-BRATS, 35
Li, 2004, Multilabel svm active learning for image classification, Image Processing, 2004. ICIP’04. 2004 International Conference on, 4, 2207, 10.1109/ICIP.2004.1421535
Wei, 2014, Cnn: Single-label to multi-label, arXiv preprint arXiv:1406.5726
Lee, 2015, Why m heads are better than one: Training a diverse ensemble of deep networks, arXiv preprint arXiv:1511.06314
Tu, 2010, Auto-context and its application to high-level vision tasks and 3d brain image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1744, 10.1109/TPAMI.2009.186
Qian, 2016, In vivo mri based prostate cancer localization with random forests and auto-context model, Computerized Medical Imaging and Graphics, 52, 44, 10.1016/j.compmedimag.2016.02.001
Dai, 2016, Instance-aware semantic segmentation via multi-task network cascades, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3150
Rahman, 2014, Ensemble classifiers and their applications: A review, arXiv preprint arXiv:1404.4088
Kim, 2015, A randomized ensemble approach to industrial ct segmentation, Proceedings of the IEEE International Conference on Computer Vision, 1707
Breiman, 2001, Random forests, Machine learning, 45, 5, 10.1023/A:1010933404324
Yijing, 2016, Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data, Knowledge-Based Systems, 94, 88, 10.1016/j.knosys.2015.11.013
Kontschieder, 2011, Structured class-labels in random forests for semantic image labelling, Computer Vision (ICCV), 2011 IEEE International Conference on, 2190, 10.1109/ICCV.2011.6126496
Zhang, 2016, Segmentation of perivascular spaces using vascular features and structured random forest from 7t mr image, International Workshop on Machine Learning in Medical Imaging, 61, 10.1007/978-3-319-47157-0_8
Zhang, 2008, Integration of multiple contextual information for image segmentation using a bayesian network, Computer Vision and Pattern Recognition Workshops, 2008. CVPRW’08. IEEE Computer Society Conference on, 1
Panagiotakis, 2011, Natural image segmentation based on tree equipartition, bayesian flooding and region merging, IEEE Transactions on Image Processing, 20, 2276, 10.1109/TIP.2011.2114893
Zhang, 2011, A bayesian network model for automatic and interactive image segmentation, IEEE Transactions on Image Processing, 20, 2582, 10.1109/TIP.2011.2121080
Yang, 2015, Multi-feature max-margin hierarchical bayesian model for action recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1610
Masoudi-Nejad, 2015, Cancer modeling and network biology: Accelerating toward personalized medicine, Seminars in cancer biology, 30, 1, 10.1016/j.semcancer.2014.06.005
Petousis, 2016, Prediction of lung cancer incidence on the low-dose computed tomography arm of the national lung screening trial: a dynamic bayesian network, Artificial intelligence in medicine, 72, 42, 10.1016/j.artmed.2016.07.001
Prastawa, 2004, A brain tumor segmentation framework based on outlier detection, Medical image analysis, 8, 275, 10.1016/j.media.2004.06.007
Menze, 2015, The multimodal brain tumor image segmentation benchmark (BRATS), IEEE transactions on medical imaging, 34, 1993, 10.1109/TMI.2014.2377694
Achanta, 2012, Slic superpixels compared to state-of-the-art superpixel methods, IEEE transactions on pattern analysis and machine intelligence, 34, 2274, 10.1109/TPAMI.2012.120
Dundar, 2007, Joint optimization of cascaded classifiers for computer aided detection, Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on, 1
Ellwaa, 2016, Brain tumor segmantation using random forest trained on iteratively selected patients, International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 129
Li, 2015, A convolutional neural network cascade for face detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5325
Zhu, 2017, Deeply-supervised CNN for prostate segmentation, Neural Networks (IJCNN), 2017 International Joint Conference on, 178, 10.1109/IJCNN.2017.7965852
Xiong, 2017, Combining local and global: Rich and robust feature pooling for visual recognition, Pattern Recognition, 62, 225, 10.1016/j.patcog.2016.08.006
Du, 2017, Stacked convolutional denoising auto-encoders for feature representation, IEEE transactions on cybernetics, 47, 1017, 10.1109/TCYB.2016.2536638
Zhang, 2016, Hierarchical feature learning with dropout k-means for hyperspectral image classification, Neurocomputing, 187, 75, 10.1016/j.neucom.2015.07.132