|
[1]Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6), 394-424. [2]Van Rijn, J. C., Reitsma, J. B., Stoker, J., Bossuyt, P. M., Van Deventer, S. J., & Dekker, E. (2006). Polyp miss rate determined by tandem colonoscopy: a systematic review. American Journal of Gastroenterology, 101(2), 343-350. [3]Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., & Vilariño, F. (2015). WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Computerized Medical Imaging and Graphics, 43, 99-111. [4]Vázquez, D., Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., López, A. M., Romero, A., ... & Courville, A. (2017). A benchmark for endoluminal scene segmentation of colonoscopy images. Journal of healthcare engineering, 2017. [5]Jha, D., Smedsrud, P. H., Riegler, M. A., Halvorsen, P., de Lange, T., Johansen, D., & Johansen, H. D. (2020, January). Kvasir-seg: A segmented polyp dataset. In International Conference on Multimedia Modeling (pp. 451-462). Springer, Cham. [6]Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham. [7]Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., & Warde-Farley, D. (2014). Generative adversarial nets in Advances in Neural Information Processing Systems (NIPS). [8]Bardhi, O., Sierra-Sosa, D., Garcia-Zapirain, B., & Elmaghraby, A. (2017, December). Automatic colon polyp detection using Convolutional encoder-decoder model. In 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) (pp. 445-448). IEEE. [9]Dutta, S., Sasmal, P., Bhuyan, M. K., & Iwahori, Y. (2018, March). Automatic Segmentation of Polyps in Endoscopic Image Using Level-Set Formulation. In 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (pp. 1-5). IEEE. [10]Akbari, M., Mohrekesh, M., Nasr-Esfahani, E., Soroushmehr, S. R., Karimi, N., Samavi, S., & Najarian, K. (2018, July). Polyp segmentation in colonoscopy images using fully convolutional network. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 69-72). IEEE. [11]Krishnan, K., Soniwal, Y., Madrosiya, A., & Desai, N. (2015, August). Colorectal polyp segmentation using front propagation on surfaces guided by shape. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3093-3096). IEEE. [12]Li, Q., Yang, G., Chen, Z., Huang, B., Chen, L., Xu, D., ... & Wang, T. (2017, October). Colorectal polyp segmentation using a fully convolutional neural network. In 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (pp. 1-5). IEEE. [13]Yan, Z., Yang, X., & Cheng, K. T. T. (2018, September). A deep model with shape-preserving loss for gland instance segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 138-146). Springer, Cham. [14]Zeiler, M. D., Krishnan, D., Taylor, G. W., & Fergus, R. (2010, June). Deconvolutional networks. In 2010 IEEE Computer Society Conference on computer vision and pattern recognition (pp. 2528-2535). IEEE. [15]LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. [16]Kohavi, R. (1995, August). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai (Vol. 14, No. 2, pp. 1137-1145). [17]McGuinness, K., & O’connor, N. E. (2010). A comparative evaluation of interactive segmentation algorithms. Pattern Recognition, 43(2), 434-444. [18]Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117. [19]Singh, V. K., Rashwan, H. A., Romani, S., Akram, F., Pandey, N., Sarker, M. M. K., Adel S., Meritxel A., Miguel A., Domenec P., & Torrents-Barrena, J. (2020). Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Systems with Applications, 139, 112855. [20]Soudani, A., & Barhoumi, W. (2019). An image-based segmentation recommender using crowdsourcing and transfer learning for skin lesion extraction. Expert Systems with Applications, 118, 400-410. [21]Ibtehaz, N., & Rahman, M. S. (2020). MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks, 121, 74-87. [22]Yu, S., Wickstrøm, K., Jenssen, R., & Principe, J. C. (2020). Understanding convolutional neural networks with information theory: An initial exploration. IEEE Transactions on Neural Networks and Learning Systems. [23]DeSantis, C. E., Ma, J., Goding Sauer, A., Newman, L. A., & Jemal, A. (2017). Breast cancer statistics, 2017, racial disparity in mortality by state. CA: a cancer journal for clinicians, 67(6), 439-448. [24]Chen, W., Zheng, R., Baade, P. D., Zhang, S., Zeng, H., Bray, F., ... & He, J. (2016). Cancer statistics in China, 2015. CA: a cancer journal for clinicians, 66(2), 115-132. [25]Norway, C. Cancer in Norway 2016-Cancer incidence, mortality, survival and prevalence in Norway. 2017. Cancer Registry of Norway: Oslo. [26]Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1125-1134). [27]Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016). Context encoders: Feature learning by inpainting. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2536-2544). [28]Milletari, F., Navab, N., & Ahmadi, S. A. (2016, October). V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV) (pp. 565-571). IEEE. [29]Jiang, H., Chen, X., Shi, F., Ma, Y., Xiang, D., Ye, L., ... & Xu, X. (2019). Improved cGAN based linear lesion segmentation in high myopia ICGA images. Biomedical optics express, 10(5), 2355-2366. [30]Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834-848. [31]Tack, A., Mukhopadhyay, A., & Zachow, S. (2018). Knee menisci segmentation using convolutional neural networks: data from the osteoarthritis initiative. Osteoarthritis and cartilage, 26(5), 680-688. [32]Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784. [33]Sorensen, T. A. (1948). A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biol. Skar., 5, 1-34. [34]Powers, D. M. (2011). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. [35]International Organization for Standardization. (1994). Accuracy (Trueness and Precision) of Measurement Methods and Results-Part 2: Basic Method for the Determination of Repeatability and Reproducibility of a Standard Measurement Method. International Organization for Standardization.
|