IEEE/CAA Journal of Automatica Sinica
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 |
[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.
|