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Supervised deep learning methods for semantic medical image segmentation are getting increasingly popular in the past few years.However, in resource constrained settings, getting large number of annotated images is very difficult as it mostly requires experts, is expensive and time-consuming.Semi-supervised segmentation can be an attractive solution where a very few labeled images are used along with a large number of unlabeled ones.
Shiraishi, J., Katsuragawa, S., Ikezoe, J., Matsumoto, T., Kobayashi, T., Komatsu, K., Matsui, M., Fujita, H., Kodera, Y., Doi, K.: Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. AJR Am J Roentgenol 174, 71–4 (Jan 2000)
2000
Earlier work this paper cites.
van Ginneken, B., Stegmann, M.B., Loog, M.: Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Medical Image Analysis 10(1), 19–40 (2006)
2006
Earlier work this paper cites.
Lee, D.H.: Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks. In: ICML Workshop on Challenges in Representation Learning, 2013 (2013)
2013
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Lecture Notes in Computer Science, pp. 234–241. Springer International Publishing (2015)
2015
Earlier work this paper cites.
2017
Earlier work this paper cites.
Baur, C., Albarqouni, S., Navab, N.: Semi-supervised Deep Learning for Fully Convolutional Networks. In: Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, pp. 311–319. Springer International Publishing (2017)
2017
Earlier work this paper cites.
Wenjia Bai, e.a.: Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation. In: Lecture Notes in Computer Science, pp. 253–260. Springer International Publishing (2017)
2017
Earlier work this paper cites.
Sedai, S., Mahapatra, D., Hewavitharanage, S., Maetschke, S., Garnavi, R.: Semi-supervised Segmentation of Optic Cup in Retinal Fundus Images Using Variational Autoencoder. In: Lecture Notes in Computer Science, pp. 75–82. Springer International Publishing (2017)
2017
Earlier work this paper cites.
Zhang, Y., Yang, L., Chen, J., Fredericksen, M., Hughes, D.P., Chen, D.Z.: Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images. In: Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, pp. 408–416. Springer International Publishing (2017)
2017
Earlier work this paper cites.
Perone, C.S., Cohen-Adad, J.: Deep Semi-supervised Segmentation with Weight-Averaged Consistency Targets. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 12–19. Springer International Publishing (2018)
2018
Earlier work this paper cites.
Nie, D., Gao, Y., Wang, L., Shen, D.: ASDNet: Attention Based Semi-supervised Deep Networks for Medical Image Segmentation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, pp. 370–378. Springer International Publishing (2018)
2018
Cited alongside, same era.
Chartsias, A., Joyce, T., Papanastasiou, G., Semple, S., Williams, M., Newby, D., Dharmakumar, R., Tsaftaris, S.A.: Factorised Spatial Representation Learning: Application in Semi-supervised Myocardial Segmentation. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, pp. 490–498. Springer International Publishing (2018)
2018
Cited alongside, same era.
Ganaye, P.A., Sdika, M., Benoit-Cattin, H.: Semi-supervised Learning for Segmentation Under Semantic Constraint. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, pp. 595–602. Springer International Publishing (2018)
2018
Cited alongside, same era.
Sohn, K., Berthelot, D., Li, C.L., Zhang, Z., Carlini, N., Cubuk, E.D., Kurakin, A., Zhang, H., Raffel, C.: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence (2020)
2020
Later among the works it cites.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A Simple Framework for Contrastive Learning of Visual Representations (2020)
2020
Later among the works it cites.
Grill, J.B.: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (2020)
2020
Later among the works it cites.
Li, X., Yu, L., Chen, H., Fu, C.W., Xing, L., Heng, P.A.: Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation. IEEE Transactions on Neural Networks and Learning Systems pp. 1–12 (2020)
2020
Later among the works it cites.
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2018
Cited alongside, same era.
Bortsova, G., Dubost, F., Hogeweg, L., Katramados, I., de Bruijne, M.: Semi-supervised Medical Image Segmentation via Learning Consistency Under Transformations. In: Lecture Notes in Computer Science, pp. 810–818. Springer International Publishing (2019)
2019
Cited alongside, same era.
Leclerc, S., Erik Smistad, e.a.: Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography. IEEE Transactions on Medical Imaging 38(9), 2198–2210 (sep 2019)
2019
Cited alongside, same era.
Cui, W., Liu, Y., Li, Y., Guo, M., Li, Y., Li, X., Wang, T., Zeng, X., Ye, C.: Semi-supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model. In: Lecture Notes in Computer Science, pp. 554–565. Springer International Publishing (2019)
2019
Cited alongside, same era.
Yu, L., Wang, S., Li, X., Fu, C.W., Heng, P.A.: Uncertainty-Aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation. In: Lecture Notes in Computer Science, pp. 605–613. Springer International Publishing (2019)
2019
Cited alongside, same era.
Bokhovkin, A., Burnaev, E.: Boundary loss for remote sensing imagery semantic segmentation (2019)
2019
Cited alongside, same era.
Tan, M., Le, Q.V.: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019)
2019
Cited alongside, same era.
Orlando, J.I., Fu, H., Barbosa Breda, J., van Keer, K., Bathula, D.R., Diaz-Pinto, A., Fang, R., Heng, P.A., Kim, J., Lee, J., Lee, J., Li, X., Liu, P., Lu, S., Murugesan, B., Naranjo, V., Phaye, S.S.R., Shankaranarayana, S.M., Sikka, A., Son, J., van den Hengel, A., Wang, S., Wu, J., Wu, Z., Xu, G., Xu, Y., Yin, P., Li, F., Zhang, X., Xu, Y., Bogunović, H.: Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical Image Analysis 59, 101570 (2020)
2020
Later among the works it cites.
Ouali, Y., Hudelot, C., Tami, M.: An Overview of Deep Semi-Supervised Learning (2020)
2020
Later among the works it cites.
Mottaghi, A., Yeung, S.: Semi-supervised segmentation of brain mri images (2020)
2020
Later among the works it cites.
Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations (2020)
2020
Later among the works it cites.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, pp. 1597–1607. PMLR (13–18 Jul 2020), http://proceedings.mlr.press/v119/chen20j.html
2020
Later among the works it cites.
Kim, B., Choo, J., Kwon, Y.D., Joe, S., Min, S., Gwon, Y.: Selfmatch: Combining contrastive self-supervision and consistency for semi-supervised learning (2021)
2021
Later among the works it cites.