Ronneberger, O., al: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI (2015)

Wang, Z., Voiculescu, I.: Dealing with unreliable annotations: a noise-robust network for semantic segmentation through a transformer-improved encoder and convolution decoder. Applied Sciences 13(13), 7966 (2023)

Article  CAS  Google Scholar 

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

Wang, Z., Voiculescu, I.: Weakly supervised medical image segmentation through dense combinations of dense pseudo-labels. In: MICCAI Workshop on Data Engineering in Medical Imaging, pp. 1–10 (2023). Springer

Chaurasia, A., Culurciello, E.: Linknet: Exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1–4 (2017). IEEE

Zhang, Z., Li, S., Wang, Z., Lu, Y.: A novel and efficient tumor detection framework for pancreatic cancer via ct images. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1160–1164 (2020). IEEE

Wang, Z., Dong, N., Voiculescu, I.: Computationally-efficient vision transformer for medical image semantic segmentation via dual pseudo-label supervision. In: 2022 IEEE International Conference on Image Processing (ICIP), pp. 1961–1965 (2022). IEEE

Sun, S., Ren, W., Wang, T., Cao, X.: Rethinking image restoration for object detection. Advances in Neural Information Processing Systems 35, 4461–4474 (2022)

Wang, Y., Jin, X., Castro, C.: Accelerating the characterization of dynamic dna origami devices with deep neural networks. Scientific Reports 13(1), 15196 (2023)

Sun, S., Ren, W., Li, J., Zhang, K., Liang, M., Cao, X.: Event-aware video deraining via multi-patch progressive learning. IEEE Transactions on Image Processing (2023)

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

Zhang, D., Zhou, F.: Self-supervised image denoising for real-world images with context-aware transformer. IEEE Access 11, 14340–14349 (2023)

Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)

Wang, Z., Su, M., Zheng, J.-Q., Liu, Y.: Densely connected swin-unet for multiscale information aggregation in medical image segmentation. In: 2023 IEEE International Conference on Image Processing (ICIP), pp. 940–944 (2023). IEEE

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022)

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017)

Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)

Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. In: MICCAI (2016). Springer

Milletari, F., Navab, N., Ahmadi, S.-A.: V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation (2016)

Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)

Ibtehaz, N., Rahman, M.S.: Multiresunet: Rethinking the u-net architecture for multimodal biomedical image segmentation. Neural networks 121, 74–87 (2020)

Wang, Z., Voiculescu, I.: Triple-view feature learning for medical image segmentation. In: MICCAI Workshop on Resource-Efficient Medical Image Analysis, pp. 42–54 (2022). Springer

Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, pp. 3–11 (2018). Springer

Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.-W., Wu, J.: Unet 3+: A full-scale connected unet for medical image segmentation. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055–1059 (2020). IEEE

Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)

Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods (2021)

Zhou, F., Fu, Z., Zhang, D.: High dynamic range imaging with context-aware transformer. In: 2023 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2023). IEEE

Wang, Z., Zhang, H., Liu, Y.: Weakly-supervised self-ensembling vision transformer for mri cardiac segmentation. In: 2023 IEEE Conference on Artificial Intelligence (CAI), pp. 101–102 (2023). IEEE

Wang, Z., Yang, C.: Mixsegnet: Fusing multiple mixed-supervisory signals with multiple views of networks for mixed-supervised medical image segmentation. Engineering Applications of Artificial Intelligence 133, 108059 (2024)

Wang, Z., Li, T., Zheng, J.-Q., Huang, B.: When cnn meet with vit: Towards semi-supervised learning for multi-class medical image semantic segmentation. In: European Conference on Computer Vision, pp. 424–441 (2022). Springer

Wang, Z., Zhao, C., Ni, Z.: Adversarial vision transformer for medical image semantic segmentation with limited annotations. British Machine Vision Conference (2022)

Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)

Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40(4), 834–848 (2017)

Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764–773 (2017)

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, pp. 1–9 (2015)

Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

Lyu, W., Zheng, S., Ling, H., Chen, C.: Backdoor attacks against transformers with attention enhancement. In: ICLR 2023 Workshop on Backdoor Attacks and Defenses in Machine Learning (2023)

Lyu, W., Dong, X., Wong, R., Zheng, S., Abell-Hart, K., Wang, F., Chen, C.: A multimodal transformer: Fusing clinical notes with structured ehr data for interpretable in-hospital mortality prediction. In: AMIA Annual Symposium Proceedings, vol. 2022, p. 719 (2022). American Medical Informatics Association

Liu, Q., Deng, H., Lian, C., Chen, X., Xiao, D., Ma, L., Chen, X., Kuang, T., Gateno, J., Yap, P.-T., et al: Skullengine: a multi-stage cnn framework for collaborative cbct image segmentation and landmark detection. In: Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 12, pp. 606–614 (2021). Springer

Wang, Z., Ma, C.: Dual-contrastive dual-consistency dual-transformer: A semi-supervised approach to medical image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 870–879 (2023)

Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114 (2019). PMLR

Zhang, S., Tong, H., Xu, J., Maciejewski, R.: Graph convolutional networks: a comprehensive review. Computational Social Networks 6(1), 1–23 (2019)

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

Chen, X., Wu, J., Lyu, W., Zou, Y., Thung, K.-H., Liu, S., Wu, Y., Ahmad, S., Yap, P.-T.: Brain tissue segmentation across the human lifespan via supervised contrastive learning. arXiv preprint arXiv:2301.01369 (2023)

Han, Z., Jian, M., Wang, G.-G.: Convunext: An efficient convolution neural network for medical image segmentation. Knowledge-Based Systems 253, 109512 (2022)

Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 (2016)

Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015). pmlr

Wang, Z., Voiculescu, I.: Quadruple augmented pyramid network for multi-class covid-19 segmentation via ct. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 2956–2959 (2021). IEEE

Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)

Sirinukunwattana, K., Pluim, J.P., Chen, H., Qi, X., Heng, P.-A., Guo, Y.B., Wang, L.Y., Matuszewski, B.J., Bruni, E., Sanchez, U., et al: Gland segmentation in colon histology images: The glas challenge contest. Medical image analysis 35, 489–502 (2017)

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019)

Kingma, D.P., Ba, J.A., Adam, J.: A method for stochastic optimization. arxiv 2014. arXiv preprint arXiv:1412.6980106 (2020)

Kirillov, A., He, K., Girshick, R., Dollár, P.: A unified architecture for instance and semantic segmentation. In: CVPR (2017)

Li, H., Xiong, P., An, J., Wang, L.: Pyramid attention network for semantic segmentation. arXiv preprint arXiv:1805.10180 (2018)

Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818 (2018)

Wang, Z., Zhang, Z., Voiculescu, I.: Rar-u-net: a residual encoder to attention decoder by residual connections framework for spine segmentation under noisy labels. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 21–25 (2021). IEEE

Valanarasu, J.M.J., Patel, V.M.: Unext: Mlp-based rapid medical image segmentation network. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 23–33 (2022). Springer

Lin, H.-Y., Liu, H.-W., et al: Multitask deep learning for segmentation and lumbosacral spine inspection. IEEE Transactions on Instrumentation and Measurement 71, 1–10 (2022)

Qi, Y., He, Y., Qi, X., Zhang, Y., Yang, G.: Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6070–6079 (2023)

Chen, B., Liu, Y., Zhang, Z., Lu, G., Kong, A.W.K.: Transattunet: Multi-level attention-guided u-net with transformer for medical image segmentation. IEEE Transactions on Emerging Topics in Computational Intelligence (2023)