[Boundary ...] [Registration ..] [YOLO ...] ... Click for details
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Topic: Efficient Boundary Detection from Deep Object Features PPT
Reportor: Shaowen Zeng
Date: 2017-05-26
Reference:

[1]Bertasius G, Shi J, Torresani L. High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and Its Applications to High-Level Vision[C]// IEEE International Conference on Computer Vision. IEEE, 2015:504-512.
Topic: Image Registration Framework by Unsupervised Deep Feature Representations Learning PPT
Reportor: Shaowen Zeng
Date: 2017-05-26
Reference:

[1]Wu G, Kim M, Wang Q, et al. Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning[J]. Deep Learning for Medical Image Analysis, 2015, 63(7):1505-1516.
Topic: YOLO:You Only Look Once: Unified, Real-Time Object Detection PPT
Reportor: Jiahong Ouyang
Date: 2017-05-26
Reference:

[1]J. Redmon, S. Divvala, R. Girshick and A. Farhadi, You only look once: Unified, real-time object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, 2016.
[Captions ...] [Vessel Extraction ..] [Semantic ...] [Global ...] ... Click for details
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Topic: From Captions to Visual Concepts and Back PPT
Reportor: Ziyan Li
Date: 2017-05-19
Reference:

[1]Fang H, Gupta S, Iandola F, et al. From captions to visual concepts and back[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 1473-1482.
Topic: Vessel Extraction in X-Ray Angiograms Using Deep Learning PPT
Reportor: Ziyan Li
Date: 2017-05-19
Reference:

[1]Nasr-Esfahani E, Samavi S, Karimi N, et al. Vessel extraction in X-ray angiograms using deep learning[C]//Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the. IEEE, 2016: 643-646.
Topic: Learning Deconvolution Network for Semantic Segmentation PPT
Reportor: Qiaojun Feng
Date: 2017-05-19
Reference:

[1]Noh H, Hong S, Han B, et al. Learning Deconvolution Network for Semantic Segmentation[C]. international conference on computer vision, 2015: 1520-1528.
Topic: Combining Global and Minutia Deep Features for Partial High-Resolution Fingerprint Combining Global and Minutia Deep Features for Partial High-Resolution Fingerprint Matching PPT
Reportor: Weixiang Chen
Date: 2017-05-19
Reference:

[1]
[MICCI 2016] ... Click for details
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Topic: Shape Model in Medical Imaging Segmentation PPT
Reportor: Qiaojun Feng
Date: 2017-05-12
Reference:

[1]Korez, R., Likar, B., Pernu, F., & Vrtovec, T. (2016, October). Model-Based Segmentation of Vertebral Bodies from MR Images with 3D CNNs. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention(pp. 433-441). Springer International Publishing.

[2]Li, Y., Ho, C. P., Chahal, N., Senior, R., & Tang, M. X. (2016, October). Myocardial Segmentation of Contrast Echocardiograms Using Random Forests Guided by Shape Model. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention(pp. 158-165). Springer International Publishing.
Topic: Multi-task Shape Regression for Medical Image Segmentation PPT
Reportor: Honghui Liu
Date: 2017-05-12
Reference:

[1]Zhen, X., Yin, Y., Bhaduri, M., Nachum, I. B., Laidley, D., & Li, S. (2016, October). Multi-task shape regression for medical image segmentation. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention(pp. 210-218). Springer International Publishing.
Topic: Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks PPT
Reportor: Zishun Feng
Date: 2017-05-12
Reference:

[1]Nie, D., Cao, X., Gao, Y., Wang, L., & Shen, D. (2016, October). Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks. InInternational Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis(pp. 170-178). Springer International Publishing.
Topic: Coronary Centerline Extraction via Optimal Flow Paths and CNN Path Pruning PPT
Reportor: Shan Gu
Date: 2017-05-12
Reference:

[1]Glsn, M. A., Funka-Lea, G., Sharma, P., Rapaka, S., & Zheng, Y. (2016, October). Coronary Centerline Extraction via Optimal Flow Paths and CNN Path Pruning. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention(pp. 317-325). Springer International Publishing.
[Binary ...] [Generative ..] [Lumen ...] [Sparsemax ...] ... Click for details
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Topic: Binary Hash Codes for Fast Image Retrieval PPT
Reportor: Weixiang Chen
Date: 2017-04-28
Reference:

[1]Lin K, Yang H F, Hsiao J H, et al. Deep learning of binary hash codes for fast image retrieval[C]//Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2015: 27-35.
Topic: Generative Adversarial Nets PPT
Reportor: Yongjie Duan
Date: 2017-04-28
Reference:

[1]Goodfellow I J, Pougetabadie J, Mirza M, et al. Generative adversarial nets[C]. neural information processing systems, 2014: 26722680.

[2]E. Denton, S. Chintala, A. Szlam, and R. Fergus. Deep generative image models using a laplacian pyramid of adversarial networks

[3]M. Mirza and S. Osindero. Conditional generative adversarial nets. CoRR, abs/1411.1784, 2014.
Topic: Lumen segmentation in CTA PPT
Reportor: Yongjie Duan
Date: 2017-04-28
Reference:

[1]Lugauer, Felix, et al. "Precise lumen segmentation in coronary computed tomography angiography." International MICCAI Workshop on Medical Computer Vision. Springer International Publishing, 2014.
Topic: Sparsemax & Video classification PPT
Reportor: Chunze Lin
Date: 2017-04-25
Reference:

[1]Martins, A., & Astudillo, R. (2016, June). From softmax to sparsemax: A sparse model of attention and multi-label classification. In International Conference on Machine Learning (pp. 1614-1623).

[2]Fernando, B., & Gould, S. (2016). Learning end-to-end video classification with rank-pooling. In Proc. of the International Conference on Machine Learning (ICML).
[Dictionaries ...] [R-CNN ...] [Heart ...] [uRNN&Graph ...] ... Click for details
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Topic: Dictionaries Based Latent Fingerprint Analysis PPT
Reportor: Qihao Yin
Date: 2017-04-21
Reference:

[1]Yang X, Feng J, Zhou J, et al. Detection and segmentation of latent fingerprints[C]//Information Forensics and Security (WIFS), 2015 IEEE International Workshop on. IEEE, 2015: 1-6.

[2]Yang X, Feng J, Zhou J. Localized dictionaries based orientation field estimation for latent fingerprints[J]. IEEE transactions on pattern analysis and machine intelligence, 2014, 36(5): 955-969.

[3]Feng J, Zhou J, Jain A K. Orientation field estimation for latent fingerprint enhancement[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(4): 925-940.
Topic: R-CNN in object R-CNN in Object Detection PPT
Reportor: Honghui Liu
Date: 2017-04-21
Reference:

[1]Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 580-587.

[2]Ross Girshick. "Fast R-CNN". ICCV 2015

[3]Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun. "Faster R-CNN: Towards Real--?Time Object Detection with Region Proposal Networks". NIPS 2015
Topic: Heart Modeling and Automatic Segmentation PPT
Reportor: Shan Gu
Date: 2017-04-21
Reference:

[1]Zheng Y, Barbu A, Georgescu B, et al. Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features[J]. IEEE transactions on medical imaging, 2008, 27(11): 1668-1681.
Topic: uRNN&Graph PPT
Reportor: Xin Yuan
Date: 2017-04-18
Reference:

[1]Unitary Evolution Recurrent Neural Networks (ICML 16, Bengio et al)

[2]Learning Convolutional Neural Networks for Graphs(ICML 16, NEC Lab)