Fetching the paper…
Reading the bibliography…
While deep convolutional neural networks (CNN) have been successfully applied for 2D image analysis, it is still challenging to apply them to 3D anisotropic volumes, especially when the within-slice resolution is much higher than the between-slice resolution and when the amount of 3D volumes is relatively small.
Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, 2015
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Earlier work this paper cites.
U-Net: Convolutional Networks for Biomedical Image Segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Earlier work this paper cites.
Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation
T. Brosch, L. Y. W. Tang, Y. Yoo, D. K. B. Li, A. Traboulsee, and R. Tam · 2016
Earlier work this paper cites.
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation, 2016
J. Chen, L. Yang, Y. Zhang, M. Alber, and D. Z. Chen · 2016
Earlier work this paper cites.
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Ã. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Densely Connected Convolutional Networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2016
Cited alongside, same era.
Automatic Segmentation of MR Brain Images With a Convolutional Neural Network
P. Moeskops, M. A. Viergever, A. M. Mendrik, L. S. de Vries, M. J. N. L. Benders, and I. Isgum · 2016
Cited alongside, same era.
Pyramid Scene Parsing Network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2016
Cited alongside, same era.
Cancer Facts and Figures 2017
American Cancer Society · 2017
Cited alongside, same era.
LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
A. Chaurasia and E. Culurciello · 2017
Cited alongside, same era.
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Liver Tumor Segmentation from CT Volumes
X. Li, H. Chen, X. Qi, Q. Dou, C.-W. Fu, and P. A. Heng · 2017
Closest in time.
Focal Loss for Dense Object Detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Closest in time.
Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging
F. Liu, Z. Zhou, H. Jang, A. Samsonov, G. Zhao, and R. Kijowski · 2017
Closest in time.
Large Kernel Matters – Improve Semantic Segmentation by Global Convolutional Network
C. Peng, X. Zhang, G. Yu, G. Luo, and J. Sun · 2017
Closest in time.
Learning Spatio-Temporal Representation With Pseudo-3D Residual Networks, 2017
Z. Qiu, T. Yao, and T. Mei · 2017
Closest in time.
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Robust Multi-scale Anatomical Landmark Detection in Incomplete 3D-CT Data
F. C. Ghesu, B. Georgescu, S. Grbic, A. K. Maier, J. Hornegger, and D. Comaniciu · 2017
Cited alongside, same era.
Superhuman Accuracy on the SNEMI3D Connectomics Challenge
K. Lee, J. Zung, P. Li, V. Jain, and H. S. Seung · 2017
Cited alongside, same era.
G. Wang, W. Li, S. Ourselin, and T. Vercauteren · 2017
Closest in time.
DeepEM3D: approaching human-level performance on 3D anisotropic EM image segmentation
T. Zeng, B. Wu, and S. Ji · 2017
Closest in time.