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Methods based on convolutional neural networks have improved the performance of biomedical image segmentation.
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2008
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M. Lux and M. Riegler, “Annotation of endoscopic videos on mobile devices: a bottom-up approach,” in Proceedings of the 4th ACM Multimedia Systems Conference , 2013, pp. 141–145
2013
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2015
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2015
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O. Ronneberger, P. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” in Proc. of Internat. Confer. on Med. Ima. Compu. Comput.-Assis. Interven. , 2015, pp. 234–241
2015
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C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu, “Deeply-supervised nets,” in Artifi. intelli. stat. , 2015, pp. 562–570
2015
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F. Chollet et al. , “Keras,” 2015
2015
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M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, “The importance of skip connections in biomedical image segmentation,” in Dee. learn. da. label. medi. applicat. , 2016, pp. 179–187
2016
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J. Wang, Z. Wei, T. Zhang, and W. Zeng, “Deeply-fused nets,” arXiv preprint arXiv:1605.07716 , 2016
2016
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M. Abadi et al. , “Tensorflow: A system for large-scale machine learning,” in 12th { \{ USENIX } \} sympo. operat. syst. desi. implement. ( { \{ OSDI } \} 16) , 2016, pp. 265–283
2016
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G. Litjens et al. , “A survey on deep learning in medical image analysis,” Med. Imag. Anal. , vol. 42, pp. 60–88, 2017
2017
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D. Shen, G. Wu, and H.-I. Suk, “Deep learning in medical image analysis,” Ann. Rev. Biomed. Eng. , vol. 19, pp. 221–248, 2017
2017
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H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in Proc. of Comput. Vis. and Patt. Recogn. , 2017, pp. 2881–2890
2017
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L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE Trans. on Patt. Analy. and Mach. Intelli. , vol. 40, no. 4, pp. 834–848, 2017
2017
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V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Trans. on Patt. Analy. and Mach. Intelli. , vol. 39, no. 12, pp. 2481–2495, 2017
2017
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T. Zhang, G.-J. Qi, B. Xiao, and J. Wang, “Interleaved group convolutions,” in Proc. of Internat. Conf. Compu. Vis. , 2017, pp. 4373–4382
2017
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B. Lim, S. Son, H. Kim, S. Nah, and K. Mu Lee, “Enhanced deep residual networks for single image super-resolution,” in Proc. of Comput. Vis. and Patt. Recogn. Worksh. , 2017, pp. 136–144
2017
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2017
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N. C. Codella et al. , “Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic),” in Proc. of Internat. Sympo. on Biomed. Imag. , 2018, pp. 168–172
2018
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P. Tschandl, C. Rosendahl, and H. Kittler, “The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Scienti. Da. , vol. 5, p. 180161, 2018
2018
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2018
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Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “UNet++: A nested U-Net architecture for medical image segmentation,” in Deep learn. med. ima. anal. multimo. learn. clini. deci. sup. , 2018, pp. 3–11
2018
Cited alongside, same era.
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proc. of the Europ. conf. comput. vis. , 2018, pp. 801–818
2018
Cited alongside, same era.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proc. of Comput. Vis. and Patt. Recogn. , 2018, pp. 7132–7141
2018
Cited alongside, same era.
Z. Zhang, Q. Liu, and Y. Wang, “Road extraction by deep residual U-Net,” IEEE Geosci. and Remo. Sens. Lett. , vol. 15, no. 5, pp. 749–753, 2018
2018
Cited alongside, same era.
D. Lin et al. , “Zigzagnet: Fusing top-down and bottom-up context for object segmentation,” in Proc. of Comput. Vis. and Patt. Recogn. , 2019, pp. 7490–7499
2019
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C. Gilvary, N. Madhukar, J. Elkhader, and O. Elemento, “The missing pieces of artificial intelligence in medicine,” Tren. pharmacolo. sci. , vol. 40, no. 8, pp. 555–564, 2019
2019
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T. Roß et al. , “Comparative validation of multi-instance instrument segmentation in endoscopy: results of the robust-mis 2019 challenge,” Med. Imag. Anal. , p. 101920, 2020
2020
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D. Jha, M. A. Riegler, D. Johansen, P. Halvorsen, and H. D. Johansen, “DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation,” in Proc. of Internat. Sympo. Comp.-Bas. Med. Syst. , 2020
2020
Later among the works it cites.
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2018
Cited alongside, same era.
J. Dolz, K. Gopinath, J. Yuan, H. Lombaert, C. Desrosiers, and I. B. Ayed, “Hyperdense-net: a hyper-densely connected cnn for multi-modal image segmentation,” IEEE Trans. Med. Imag. , vol. 38, no. 5, pp. 1116–1126, 2018
2018
Cited alongside, same era.
M. Yang, K. Yu, C. Zhang, Z. Li, and K. Yang, “Denseaspp for semantic segmentation in street scenes,” in Proc. of Comput. Vis. and Patt. Recogn. , 2018, pp. 3684–3692
2018
Cited alongside, same era.
2018
Cited alongside, same era.
A. C. Society, “Cancer facts & figures 2018,” 2018
2018
Cited alongside, same era.
M. E. Celebi, N. Codella, and A. Halpern, “Dermoscopy image analysis: overview and future directions,” IEEE J. Biomed. Health Inform , vol. 23, no. 2, pp. 474–478, 2019
2019
Cited alongside, same era.
J. C. Caicedo et al. , “Nucleus segmentation across imaging experiments: the 2018 data science bowl,” Nat. Meth. , vol. 16, no. 12, pp. 1247–1253, 2019
2019
Cited alongside, same era.
Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “UNet++: Redesigning skip connections to exploit multiscale features in image segmentation,” IEEE Trans. Med. Imag. , vol. 39, no. 6, pp. 1856–1867, 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
N. Ibtehaz and M. S. Rahman, “Multiresunet: Rethinking the u-net architecture for multimodal biomedical image segmentation,” Neur. Networ. , vol. 121, pp. 74–87, 2020
2020
Later among the works it cites.
J. Sun, F. Darbehani, M. Zaidi, and B. Wang, “Saunet: shape attentive u-net for interpretable medical image segmentation,” in Proc. of Internat. Confer. on Med. Ima. Compu. Comput.-Assis. Interven. , 2020, pp. 797–806
2020
Later among the works it cites.
T. Hassan, M. Usman Akram, and N. Werghi, “Evaluation of Deep Segmentation Models for the Extraction of Retinal Lesions from Multi-modal Retinal Images,” arXiv e-prints , 2020
2020
Later among the works it cites.
Y. Guo, J. Bernal, and B. J Matuszewski, “Polyp segmentation with fully convolutional deep neural networks — extended evaluation study,” Jour. of Imag. , vol. 6, no. 7, p. 69, 2020
2020
Later among the works it cites.
D.-P. Fan et al. , “PraNet: parallel reverse attention network for polyp segmentation,” in Proc. of Internat. Confer. on Med. Ima. Compu. Comput.-Assis. Interven. , 2020, pp. 263–273
2020
Later among the works it cites.
L. Liu et al. , “A survey on u-shaped networks in medical image segmentations,” Neurocomputing , vol. 409, pp. 244–258, 2020
2020
Later among the works it cites.
J. Wang and other, “Deep high-resolution representation learning for visual recognition,” IEEE Trans. on Patt. Analy. Mach. Intelli. , p. 1–1, 2020
2020
Later among the works it cites.
N. K. Tomar et al. , “DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation,” in Proc. of the ICPR 2020 Worksh. and Chall. , 2020
2020
Later among the works it cites.
J. Wang et al. , “Deep high-resolution representation learning for visual recognition,” IEEE trans. patt. analy. mach. , 2020
2020
Later among the works it cites.
S. Ali et al. , “Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy,” Med. Imag. Anal. , p. 102002, 2021
2021
Closest in time.
D. Jha et al. , “Real-Time Polyp Detection, Localisation and Segmentation in Colonoscopy Using Deep Learning,” IEEE Acc. , 2021
2021
Closest in time.
2021
Closest in time.
D. Sarvamangala and R. V. Kulkarni, “Convolutional neural networks in medical image understanding: a survey,” Evolutionary intelligence , pp. 1–22, 2021
2021
Closest in time.
D. Jha and Others, “A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation,” IEEE J. Biomed. Health Inform , 2021
2021
Closest in time.
Z. Xu, W. Zhang, T. Zhang, and J. Li, “Hrcnet: high-resolution context extraction network for semantic segmentation of remote sensing images,” Remote Sensing , vol. 13, no. 1, p. 71, 2021
2021
Closest in time.