Abstract
Landmark localization plays an important role in medical image analysis. Learning based methods, including convolutional neural network (CNN) and graph convolutional network (GCN), have demonstrated the state-of-the-art performance. However, most of these methods are fully-supervised and heavily rely on manual labeling of a large training dataset. In this paper, based on a fully-supervised graph-based method, deep adaptive graph (DAG), we proposed a semi-supervised extension of it, termed few-shot DAG, i.e., five-shot DAG. It first trains a DAG model on the labeled data and then fine-tunes the pre-trained model on the unlabeled data with a teacher-student semi-supervised learning (SSL) mechanism. In addition to the semi-supervised loss, we propose another loss using Jensen–Shannon (JS) divergence to regulate the consistency of the intermediate feature maps. We extensively evaluated our method on pelvis, hand and chest landmark detection tasks. Our experiment results demonstrate consistent and significant improvements over previous methods.
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References
Cui, W., et al.: Semi-supervised brain lesion segmentation with an adapted mean teacher model. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 554–565. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_43
Honari, S., Molchanov, P., Tyree, S., Vincent, P., Pal, C., Kautz, J.: Improving landmark localization with semi-supervised learning. In: CVPR, pp. 1546–1555 (2018)
Juneja, M., et al.: A review on cephalometric landmark detection techniques. Biomed. Signal Process. Control 66, 102486 (2021)
Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242 (2016)
Lee, D.H.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on challenges in representation learning, ICML, vol. 3 (2013)
Li, W., et al.: Structured landmark detection via topology-adapting deep graph learning. arXiv preprint arXiv:2004.08190 (2020)
Lu, Y., et al.: Learning to segment anatomical structures accurately from one exemplar. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 678–688. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_66
Lv, J., Shao, X., Xing, J., Cheng, C., Zhou, X.: A deep regression architecture with two-stage re-initialization for high performance facial landmark detection. In: CVPR, pp. 3317–3326 (2017)
Payer, C., Štern, D., Bischof, H., Urschler, M.: Integrating spatial configuration into heatmap regression based cnns for landmark localization. Media 54, 207–219 (2019)
Raju, A., et al.: Co-heterogeneous and adaptive segmentation from multi-source and multi-phase CT imaging data: a study on pathological liver and lesion segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 448–465. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58592-1_27
Sohn, K., Zhang, Z., Li, C.L., Zhang, H., Lee, C.Y., Pfister, T.: A simple semi-supervised learning framework for object detection. arXiv preprint arXiv:2005.04757 (2020)
Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: CVPR, pp. 5693–5703 (2019)
Tang, X., Guo, F., Shen, J., Du, T.: Facial landmark detection by semi-supervised deep learning. Neurocomputing 297, 22–32 (2018)
Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: NeurIPS, pp. 1195–1204 (2017)
Trigeorgis, G., Snape, P., Nicolaou, M.A., Antonakos, E., Zafeiriou, S.: Mnemonic descent method: a recurrent process applied for end-to-end face alignment. In: CVPR, pp. 4177–4187 (2016)
Valle, R., Buenaposada, J.M., Valdes, A., Baumela, L.: A deeply-initialized coarse-to-fine ensemble of regression trees for face alignment. In: ECCV, pp. 585–601 (2018)
Wang, Y., et al.: Knowledge distillation with adaptive asymmetric label sharpening for semi-supervised fracture detection in chest x-rays. arXiv preprint arXiv:2012.15359 (2020)
Wu, Y., Ji, Q.: Facial landmark detection: a literature survey. IJCV 127(2), 115–142 (2019)
Yu, X., Zhou, F., Chandraker, M.: Deep deformation network for object landmark localization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 52–70. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_4
Zhang, Z., Luo, P., Loy, C.C., Tang, X.: Learning deep representation for face alignment with auxiliary attributes. TPAMI 38(5), 918–930 (2015)
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Zhou, XY. et al. (2021). Scalable Semi-supervised Landmark Localization for X-ray Images Using Few-Shot Deep Adaptive Graph. In: Engelhardt, S., et al. Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. DGM4MICCAI DALI 2021 2021. Lecture Notes in Computer Science(), vol 13003. Springer, Cham. https://doi.org/10.1007/978-3-030-88210-5_13
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