201710-A Deep Level Set Method for Image Segmentation (paper)
pipeline
实验结果
2017 Ping Hu, Bing Shuai, Jun Liu, Gang Wang. "Deep Level Sets for Salient Object Detection". IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. (paper)
2018-Thierbach K. et al. (2018) Combining Deep Learning and Active Contours Opens The Way to Robust, Automated Analysis of Brain Cytoarchitectonics. In: Shi Y., Suk HI., Liu M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science, vol 11046. Springer, Cham (paper)
2018-Thierbach, Konstantin, et al. "Deep Learning meets Topology-preserving Active Contours: towards scalable quantitative histology of cortical cytoarchitecture." bioRxiv (2018). (paper)
201901-Deep Level Sets: Implicit Surface Representations for 3D Shape Inference (paper, author)
其他的CNN与模型融合的方法
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Esophagus segmentation in CT via 3D fully convolutional neural network and random walk Fechter, T. , Adebahr, S. , Baltas, D. , Ben Ayed, I. , Desrosiers, C. and Dolz, J. (2017), Med. Phys.
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Size-constraint loss for weakly supervised CNN segmentation. MIDL 2018, Oral.
Hoel Kervadec, Jose Dolz, Meng Tang, Eric Granger, Yuri Boykov, Ismail Ben Ayed
[PDF] [CODE] [BibTex] [talk] -
Beyond Gradient Descent for Regularized Segmentation Losses
Dmitrii Marin, Meng Tang, Ismail Ben Ayed, Yuri Boykov
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, June 2019. [PDF coming soon]
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On Regularized Losses for Weakly-supervised CNN Segmentation
Meng Tang, Federico Perazzi, Abdelaziz Djelouah, Ismail Ben Ayed, Christopher Schroers, Yuri Boykov
European Conference on Computer Vision (ECCV), Munich, Germany, September 2018. [PDF] [Code] [arXiv]
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