A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 9 Issue 7
Jul.  2022

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
W. X. He, M. Liu, Y. Tang, Q. H. Liu, and Y. N. Wang, “Differentiable automatic data augmentation by proximal update for medical image segmentation,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1315–1318, Jul. 2022. doi: 10.1109/JAS.2022.105701
Citation: W. X. He, M. Liu, Y. Tang, Q. H. Liu, and Y. N. Wang, “Differentiable automatic data augmentation by proximal update for medical image segmentation,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1315–1318, Jul. 2022. doi: 10.1109/JAS.2022.105701

Differentiable Automatic Data Augmentation by Proximal Update for Medical Image Segmentation

doi: 10.1109/JAS.2022.105701
More Information
  • loading
  • [1]
    F. Isensee, F. Jaeger, S. A. A. Kohl, J. Petersen, and K. H. Maier-Hein, “nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation,” Nature Methods, vol. 18, no. 2, pp. 203–211, 2021. doi: 10.1038/s41592-020-01008-z
    [2]
    O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Proc. Med. Image Comput. Comput. -Assisted Intervention, 2015, pp. 234–241.
    [3]
    Y. Tan, M. Liu, W. Chen, X. Wang, H. Peng, and Y. Wang, “DeepBranch: Deep neural networks for branch point detection in biomedical images,” IEEE Trans. Med. Imag., vol. 39, no. 4, pp. 1195–1205, 2020. doi: 10.1109/TMI.2019.2945980
    [4]
    Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “UNet++: Redesigning skip connections to exploit multiscale features in image segmentation,” IEEE Trans. Med. Imag., vol. 39, no. 6, pp. 1856–1867, 2020. doi: 10.1109/TMI.2019.2959609
    [5]
    A. K. Bhandari, A. Ghosh, and I. Vinod Kumar, “A local contrast fusion based 3D Otsu algorithm for multilevel image segmentation,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 200–213, 2020. doi: 10.1109/JAS.2019.1911843
    [6]
    X. Wang, M. Liu, D. S. Raychaudhuri, S. Paul, Y. Wang, and A. K. Roy-Chowdhury, “Learning person re-identification models from videos with weak supervision,” IEEE Trans. Image Process., vol. 30, pp. 3017–3028, 2021. doi: 10.1109/TIP.2021.3056223
    [7]
    W. Jiang, M. Liu, Y. Peng, L. Wu, and Y. Wang, “HDCB-Net: A neural network with the hybrid dilated convolution for pixel-level crack detection on concrete bridges,” IEEE Trans. Ind. Informat., vol. 17, no. 8, pp. 5485–5494, 2021. doi: 10.1109/TII.2020.3033170
    [8]
    Y. Tang, B. Li, M. Liu, B. Chen, Y. Wang, and W. Ouyang, “AutoPedestrian: An automatic data augmentation and loss function search scheme for pedestrian detection,” IEEE Trans. Image Process., vol. 30, pp. 8483–8496, 2021. doi: 10.1109/TIP.2021.3115672
    [9]
    T. Zhang, J. Wang, and M. Q.-H. Meng, “Generative adversarial network based heuristics for sampling-based path planning,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 64–74, 2022. doi: 10.1109/JAS.2021.1004275
    [10]
    J. M. J. Valanarasu, V. A. Sindagi, I. Hacihaliloglu, and V. M. Patel, “KiU-Net: Towards accurate segmentation of biomedical images using over-complete representations,” in Proc. Med. Image Comput. Comput. -Assisted Intervention, 2020, pp. 363–373.
    [11]
    D. Yang, H. Roth, Z. Xu, F. Milletari, L. Zhang, and D. Xu, “Searching learning policy with reinforcement learning for 3D medical image segmentation,” in Proc. Med. Image Comput. Comput. -Assisted Intervention, 2019, pp. 3–11.
    [12]
    J. Xu, M. Li, and Z. Zhu, “Automatic data augmentation for 3D medical image segmentation,” in Proc. Med. Image Comput. Comput. -Assisted Intervention, 2020, pp. 378–387.
    [13]
    Y. Li, G. Hu, and Y. Wang, “DADA: Differentiable automatic data augmentation,” in Proc. Eur. Conf. Comput. Vis., 2020, pp. 580–595.
    [14]
    A. Buslaev, A. Parinov, E. Khvedchenya, V. I. Iglovikov, and A. A. Kalinin, “Albumentations: Fast and flexible image augmentations,” arXiv preprint arXiv: 1809.06839, 2018.
    [15]
    E. Jang, S. X. Gu, and B. Poole, “Categorical reparameterization with gumbel-softmax,” in Proc. Int. Conf. Learn. Representations, 2017.
    [16]
    Y. Bengio, N. Leonard, and A. Courville, “Estimating or propagating gradients through stochastic neurons for conditional computation,” arXiv preprint arXiv: 1308.3432, 2013.
    [17]
    Q. Yao, J. Xu, W. Tu, and Z. Zhu, “Efficient neural architecture search via proximal iterations,” in Proc. AAAI Conf. Artif. Intell., 2020, pp. 6664–6671.
    [18]
    T. Fan, G. Wang, Y. Li, and H. Wang, “MA-Net: A multi-scale sttention network for liver and tumor segmentation,” IEEE Access, vol. 8, pp. 179656–179665, 2020. doi: 10.1109/ACCESS.2020.3025372
    [19]
    T.-Y. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie, “Feature pyramid networks for object detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2017, pp. 936–944.
    [20]
    G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2017, pp. 2261–2269.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(2)  / Tables(6)

    Article Metrics

    Article views (483) PDF downloads(68) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return