MDU-Net: multi-scale densely connected U-Net for biomedical image segmentation

Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. p. 3431–3440.

Guan S, Khan A, Sikdar S, Chitnis P.V. Fully dense unet for 2d sparse photoacoustic tomography artifact removal 2018.

Dong L, He L, Mao M, Kong G, Wu X, Zhang Q, Cao X, Izquierdo E. Cunet: a compact unsupervised network for image classification. IEEE Transactions on Multimedia. 2018;20(8):2012–21.

Google Scholar 

Raza SEA, Cheung L, Epstein D, Pelengaris S, Khan M, Rajpoot N.M. Mimo-net: a multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). 201. p. 337–340.

Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging. 2019;39(6):1856–67.

Article  Google Scholar 

Yin XX, Sun L, Fu Y, Lu R, Zhang Y. U-net-based medical image segmentation. J Healthc Eng. 2022. https://doi.org/10.1155/2022/4189781.

Article  Google Scholar 

Farabet C, Couprie C, Najman L, LeCun Y. Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013;35(8):1915–29.

Article  Google Scholar 

Newell A, Yang K, Deng J. Stacked hourglass networks for human pose estimation. In: European Conference on Computer Vision, Springer 2016. p. 483–499.

Tang Z, Peng X, Geng S, Wu L, Zhang S, Metaxas D. Quantized densely connected u-nets for efficient landmark localization. In: European Conference on Computer Vision (ECCV) 2018.

Yang W, Li S, Ouyang W, Li H, Wang X. Learning feature pyramids for human pose estimation. In: The IEEE International Conference on Computer Vision (ICCV). Volume 2. 2017.

Lin G, Milan A, Shen C, Reid ID. Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. In: Cvpr. Volume 1. 2017. p. 5.

Tan W, Liu Y, Liu H, Yang J, Yin X, Zhang Y. A segmentation method of lung parenchyma from chest ct images based on dual u-net. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE 2019. p. 1649–1656.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. p. 1–9.

Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 2818–2826.

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016. p. 770–778.

Huang G, Sun Y, Liu Z, Sedra D. Weinberger. K.Q. Deep networks with stochastic depth. 2016. p. 646–61.

Zhao H, Shi J, Qi X, Wang X, Jia J. Pyramid scene parsing network. In: IEEE Conference on Computer Vision and Pattern Recognition. 2017. p. 6230–6239.

Drozdzal M, Vorontsov E, Chartrand G, Kadoury S, Pal C. The importance of skip connections in biomedical image segmentation. 2016. p. 179–187.

Huang G, Liu Z, Maaten LVD, Weinberger KQ. Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition. 2017. p. 2261–2269.

Jégou S, Drozdzal M, Vazquez D, Romero A, Bengio Y. The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. 2017. p. 11–19.

Yang M, Yu K, Zhang C, Li Z, Yang K. Denseaspp for semantic segmentation in street scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. p. 3684–3692.

Bilinski P, Prisacariu V. Dense decoder shortcut connections for single-pass semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. p. 6596–6605.

Chen LC, Papandreou G, Schroff F, Adam H. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 2017.

Jin KH, Mccann MT, Froustey E, Unser M. Deep convolutional neural network for inverse problems in imaging. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society. 2016;26(9):4509–22.

Article  MathSciNet  MATH  Google Scholar 

Li X, Chen H, Qi X, Dou Q, Fu CW, Heng PA. H-denseunet: Hybrid densely connected unet for liver and tumor segmentation from ct volumes. IEEE Transactions on Medical Imaging. 2017. https://doi.org/10.1109/TMI.2018.2845918.

Article  Google Scholar 

Chen LC, Yang Y, Wang J, Xu W, Yuille AL. Attention to scale: scale-aware semantic image segmentation. In: Computer Vision and Pattern Recognition. 2016. p. 3640–3649.

Lin G, Shen C, Van Den Hengel A, Reid I. Efficient piecewise training of deep structured models for semantic segmentation. 2016. p. 3194–3203.

Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions. CoRR arXiv:1511.07122 2015.

Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y. Deformable convolutional networks. 2017. p. 764–773.

Zhang J, Zhang Y, Xu X. Pyramid u-net for retinal vessel segmentation. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE 2021. p. 1125–1129.

Zhang J, Zhang Y, Zhu S, Xu X. Constrained multi-scale dense connections for accurate biomedical image segmentation. In: BIBM, IEEE 2020. p. 877–884.

Al-Masni MA, Kim DH. Cmm-net: contextual multi-scale multi-level network for efficient biomedical image segmentation. Sci Rep. 2021;11(1):1–18.

Article  Google Scholar 

Jacobs JG, Panagiotaki E, Alexander DC. Gleason grading of prostate tumours with max-margin conditional random fields. In: International Workshop on Machine Learning in Medical Imaging, Springer 2014. p. 85–92.

Nguyen K, Sarkar A, Jain AK. Structure and context in prostatic gland segmentation and classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer 2012. p. 115–123.

Sirinukunwattana K, Snead DR, Rajpoot NM. A novel texture descriptor for detection of glandular structures in colon histology images. In: Medical Imaging 2015: Digital Pathology. 9420, International Society for Optics and Photonics 2015. p. 94200S.

Fu H, Qiu G, Shu J, Ilyas M. A novel polar space random field model for the detection of glandular structures. IEEE Transactions on Medical Imaging. 2014;33(3):764–76.

Article  Google Scholar 

Dhungel N, Carneiro G, Bradley AP. Deep learning and structured prediction for the segmentation of mass in mammograms. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer 2015. p. 605–612.

Dou Q, Chen H, Yu L, Zhao L, Qin J, Wang D, Mok VC, Shi L, Heng PA. Automatic detection of cerebral microbleeds from mr images via 3d convolutional neural networks. IEEE Transactions on Medical Imaging. 2016;35(5):1182–95.

Article  Google Scholar 

Roth HR, Lu L, Farag A, Shin HC, Liu J, Turkbey EB, Summers RM. Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer 2015. p. 556–564.

Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep learning with radiomics for disease diagnosis and treatment: challenges and potential. Front Oncol. 2022. https://doi.org/10.3389/fonc.2022.773840.

Article  Google Scholar 

Xiaowei X, Lu Q, Yang L, Hu S, Chen D, Hu Y, Shi Y. Quantization of fully convolutional networks for accurate biomedical image segmentation. Preprint at arXiv:1803.04907 2018.

Wen Z, Liu J, Li Y. Gcsba-net: Gabor-based and cascade squeeze bi-attention network for gland segmentation. IEEE J-BHI 2020.

Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods. 2021;18(2):203–11.

Article  Google Scholar 

Zhou A, Yao A, Guo Y, Xu L, Chen Y. Incremental network quantization: towards lossless cnns with low-precision weights. 2016.

留言 (0)

沒有登入
gif