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Deep convolutional neural networks have achieved remarkable progress on a variety of medical image computing tasks.
M. D. Abramoff, W. L. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features,”
2007
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T. Heimann and H.-P. Meinzer, “Statistical shape models for 3d medical image segmentation: a review,”
2009
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H. Jegou, M. Douze, and C. Schmid, “Product quantization for nearest neighbor search,”
2010
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X. You, Q. Peng, Y. Yuan, Y.-m. Cheung, and J. Lei, “Segmentation of retinal blood vessels using the radial projection and semi-supervised approach,”
2011
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Y. He and F. Xie, “Automatic skin lesion segmentation based on texture analysis and supervised learning,” in
2012
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M. Emre Celebi, Q. Wen, S. Hwang, H. Iyatomi, and G. Schaefer, “Lesion border detection in dermoscopy images using ensembles of thresholding methods,”
2013
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A. R. Sadri, M. Zekri, S. Sadri, N. Gheissari, M. Mokhtari, and F. Kolahdouzan, “Segmentation of dermoscopy images using wavelet networks,”
2013
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J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,”
2013
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N. M. Portela, G. D. Cavalcanti, and T. I. Ren, “Semi-supervised clustering for mr brain image segmentation,”
2014
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D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
2014
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A. Masood, A. Al-Jumaily, and K. Anam, “Self-supervised learning model for skin cancer diagnosis,” in
2015
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K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,”
2015
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O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in
2015
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
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T. Cohen and M. Welling, “Group equivariant convolutional networks,” in
2016
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S. Dieleman, J. D. Fauw, and K. Kavukcuoglu, “Exploiting cyclic symmetry in convolutional neural networks,” in
2016
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Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” in
2016
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F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in
2016
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M. Sajjadi, M. Javanmardi, and T. Tasdizen, “Regularization with stochastic transformations and perturbations for deep semi-supervised learning,” in
2016
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M. D. Kohli, R. M. Summers, and J. R. Geis, “Medical image data and datasets in the era of machine learning—whitepaper from the 2016 c-mimi meeting dataset session,”
2017
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S. Sedai, D. Mahapatra, and S. e. a. Hewavitharanage, “Semi-supervised segmentation of optic cup in retinal fundus images using variational autoencoder,” in
2017
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X. Feng, J. Yang, A. F. Laine, and E. D. Angelini, “Discriminative localization in cnns for weakly-supervised segmentation of pulmonary nodules,” in
2017
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K. Kamnitsas, C. Baumgartner, C. Ledig, V. Newcombe, J. Simpson, A. Kane
2017
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L. Gu, Y. Zheng, R. Bise, I. Sato, N. Imanishi, and S. Aiso, “Semi-supervised learning for biomedical image segmentation via forest oriented super pixels (voxels),” in
2017
Cited alongside, same era.
S. Jaisakthi, A. Chandrabose, and P. Mirunalini, “Automatic skin lesion segmentation using semi-supervised learning technique,”
2017
Cited alongside, same era.
W. Bai, O. Oktay, and M. e. a. Sinclair, “Semi-supervised learning for network-based cardiac mr image segmentation,” in
2017
Cited alongside, same era.
S. Laine and T. Aila, “Temporal ensembling for semi-supervised learning,” in
2017
Cited alongside, same era.
D. E. Worrall, S. J. Garbin, D. Turmukhambetov, and G. J. Brostow, “Harmonic networks: Deep translation and rotation equivariance,” in
2017
Cited alongside, same era.
C. S. Perone and J. Cohen-Adad, “Deep semi-supervised segmentation with weight-averaged consistency targets,” in
2018
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P.-A. Ganaye, M. Sdika, and H. Benoit-Cattin, “Semi-supervised learning for segmentation under semantic constraint,” in
2018
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X. Li, L. Yu, H. Chen, C.-W. Fu, and P.-A. Heng, “Semi-supervised skin lesion segmentation via transformation consistent self-ensembling model,”
2018
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A. Chartsias, T. Joyce, G. Papanastasiou, S. Semple, M. Williams, D. Newby, R. Dharmakumar, and S. A. Tsaftaris, “Factorised spatial representation learning: application in semi-supervised myocardial segmentation,” in
2018
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Y. Zhang, L. Yang, J. Chen, M. Fredericksen, D. P. Hughes, and D. Z. Chen, “Deep adversarial networks for biomedical image segmentation utilizing unannotated images,” in
2017
Cited alongside, same era.
L. Yu, H. Chen, Q. Dou, J. Qin, and P.-A. Heng, “Automated melanoma recognition in dermoscopy images via very deep residual networks,”
2017
Cited alongside, same era.
J. H. Tan, U. R. Acharya, S. V. Bhandary, K. C. Chua, and S. Sivaprasad, “Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network,”
2017
Cited alongside, same era.
F. Lu, F. Wu, P. Hu, Z. Peng, and D. Kong, “Automatic 3d liver location and segmentation via convolutional neural network and graph cut,”
2017
Cited alongside, same era.
D. Yang, D. Xu, S. K. Zhou, B. Georgescu, M. Chen, S. Grbic, D. Metaxas, and D. Comaniciu, “Automatic liver segmentation using an adversarial image-to-image network,” in
2017
Cited alongside, same era.
Y. Yuan, M. Chao, and Y.-C. Lo, “Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance,”
2017
Cited alongside, same era.
A. Tarvainen and H. Valpola, “Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,” in
2017
Cited alongside, same era.
2018
Later among the works it cites.
X. Li, L. Yu, C.-W. Fu, and P.-A. Heng, “Deeply supervised rotation equivariant network for lesion segmentation in dermoscopy images,” pp. 235–243, 2018
2018
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G. Chlebus, A. Schenk, J. H. Moltz, B. van Ginneken, H. K. Hahn, and H. Meine, “Automatic liver tumor segmentation in ct with fully convolutional neural networks and object-based postprocessing,”
2018
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H. Fu, J. Cheng, Y. Xu, C. Zhang, D. W. K. Wong, J. Liu, and X. Cao, “Disc-aware ensemble network for glaucoma screening from fundus image,”
2018
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X. Li, H. Chen, X. Qi, Q. Dou, C.-W. Fu, and P.-A. Heng, “H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes,”
2018
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N. C. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza
2018
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W.-C. Hung, Y.-H. Tsai, Y.-T. Liou, Y.-Y. Lin, and M.-H. Yang, “Adversarial learning for semi-supervised semantic segmentation,”
2018
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G. Venkatesh, Y. Naresh, S. Little, and N. E. O’Connor, “A deep residual architecture for skin lesion segmentation,” in
2018
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A. Oliver, A. Odena, C. Raffel, E. D. Cubuk, and I. J. Goodfellow, “Realistic evaluation of deep semi-supervised learning algorithms,”
2018
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Y. Zhou, Y. Wang, P. Tang, S. Bai, W. Shen, E. Fishman, and A. Yuille, “Semi-supervised 3d abdominal multi-organ segmentation via deep multi-planar co-training,” in
2019
Closest in time.
V. Cheplygina, M. de Bruijne, and J. P. Pluim, “Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis,”
2019
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2019
Closest in time.
H. Seo, C. Huang, M. Bassenne, R. Xiao, and L. Xing, “Modified u-net (mu-net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in ct images,”
2019
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E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, “Autoaugment: Learning augmentation strategies from data,” in
2019
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S. Lim, I. Kim, T. Kim, C. Kim, and S. Kim, “Fast autoaugment,” in
2019
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J. I. Orlando, H. Fu, J. B. Breda, K. van Keer, D. R. Bathula, A. Diaz-Pinto
2020
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A. Buslaev, V. I. Iglovikov, E. Khvedchenya, A. Parinov, M. Druzhinin, and A. A. Kalinin, “Albumentations: fast and flexible image augmentations,”
2020
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W. M. Gondal, J. M. Köhler, R. Grzeszick, G. A. Fink, and M. Hirsch, “Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images,” in
2073
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