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In recent years, deep learning (DL) methods have become powerful tools for biomedical image segmentation.
Li, K., Wu, X., Chen, D.Z., Sonka, M.: Optimal surface segmentation in volumetric images — a graph-theoretic approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(1), 119–134 (2006)
2006
Earlier work this paper cites.
Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: Advances in Neural Information Processing Systems. pp. 109–117 (2011)
2011
Earlier work this paper cites.
Liu, X., Chen, D.Z., Tawhai, M.H., Wu, X., Hoffman, E.A., Sonka, M.: Optimal graph search based segmentation of airway tree double surfaces across bifurcations. IEEE Transactions on Medical Imaging 32(3), 493–510 (2013)
2013
Earlier work this paper cites.
Dai, J., He, K., Sun, J.: BoxSup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 1635–1643 (2015)
2015
Earlier work this paper cites.
Papandreou, G., Chen, L.C., Murphy, K.P., Yuille, A.L.: Weakly- and semi-supervised learning of a deep convolutional network for semantic image segmentation. In: IEEE International Conference on Computer Vision. pp. 1742–1750 (2015)
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 234–241 (2015)
2015
Earlier work this paper cites.
Bearman, A., Russakovsky, O., Ferrari, V., Li, F.F.: What’s the point: Semantic segmentation with point supervision. In: European Conference on Computer Vision. pp. 549–565 (2016)
2016
Earlier work this paper cites.
Chen, H., Qi, X., Yu, L., Heng, P.A.: DCAN: Deep contour-aware networks for accurate gland segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 2487–2496 (2016)
2016
Cited alongside, same era.
Chen, J., Yang, L., Zhang, Y., Alber, M., Chen, D.Z.: Combining fully convolutional and recurrent neural networks for 3D biomedical image segmentation. In: Advances in Neural Information Processing Systems. pp. 3036–3044 (2016)
2016
Cited alongside, same era.
Lin, D., Dai, J., Jia, J., He, K., Sun, J.: ScribbleSup: Scribble-supervised convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 3159–3167 (2016)
2016
Cited alongside, same era.
Zhang, Y., Yang, L., MacKenzie, J.D., Ramachandran, R., Chen, D.Z.: A seeding-searching-ensemble method for gland segmentation in H&E-stained images. BMC Medical Informatics and Decision Making 16(2), 80 (2016)
2016
Cited alongside, same era.
Khoreva, A., Benenson, R., Hosang, J., Hein, M., Schiele, B.: Simple does it: Weakly supervised instance and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)
2017
Later among the works it cites.
Papadopoulos, D.P., Uijlings, J.R.R., Keller, F., Ferrari, V.: Extreme clicking for efficient object annotation. In: IEEE International Conference on Computer Vision. pp. 4940–4949 (2017)
2017
Later among the works it cites.
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., Böhm, A., Ronneberger, O., Cheikh, B.B., Racoceanu, D., Kainz, P., Pfeiffer, M., Urschler, M., Snead, D.R.J., Rajpoot, N.M.: Gland segmentation in colon histology images: The GlaS challenge contest. Medical Image Analysis 35, 489–502 (2017)
2017
Later among the works it cites.
Yang, L., Zhang, Y., Chen, J., Zhang, S., Chen, D.Z.: Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 399–407 (2017)
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2016
Cited alongside, same era.
Baur, C., Albarqouni, S., Navab, N.: Semi-supervised deep learning for fully convolutional networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 31–319 (2017)
2017
Cited alongside, same era.
2017
Later among the works it 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: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 408–416 (2017)
2017
Later among the works it cites.