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Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnosis in many heart related diseases.
Suinesiaputra, A., et al.: A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images. Medical Image Analysis. 18, 50–62 (2014). https://doi.org/10.1016/j.media.2013.09.001
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Bernard, O., et al.: Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? IEEE Transactions on Medical Imaging. 37, 2514–2525 (2018). https://doi.org/10.1109/TMI.2018.2837502
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Bjorck, N., Gomes, C.P., Selman, B., Weinberger, K.Q.: Understanding Batch Normalization. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., and Garnett, R. (eds.) Advances in Neural Information Processing Systems 31. pp. 7694–7705. Curran Associates, Inc. (2018)
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Volpi, R., Namkoong, H., Sener, O., Duchi, J.C., Murino, V., Savarese, S.: Generalizing to Unseen Domains via Adversarial Data Augmentation. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., and Garnett, R. (eds.) Advances in Neural Information Processing Systems 31. pp. 5334–5344. Curran Associates, Inc. (2018)
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Karani, N., Chaitanya, K., Baumgartner, C., Konukoglu, E.: A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., and Fichtinger, G. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. pp. 476–484. Springer International Publishing, Cham (2018)
2018
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Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J., Oermann, E.K.: Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLOS Medicine. 15, e1002683 (2018). https://doi.org/10.1371/journal.pmed.1002683
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Sandfort, V., Yan, K., Pickhardt, P.J., Summers, R.M.: Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks. Scientific Reports. 9, 16884 (2019). https://doi.org/10.1038/s41598-019-52737-x
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Chen, C., et al.: Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images. (2020). https://doi.org/10.3389/fcvm.2020.00105
2020
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Zhang, L., Wang, X., Yang, D., Sanford, T., Harmon, S., Turkbey, B., Wood, B.J., Roth, H., Myronenko, A., Xu, D., Xu, Z.: Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation. IEEE Transactions on Medical Imaging. 39, 2531–2540 (2020). https://doi.org/10.1109/TMI.2020.2973595
2020
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Isensee, F., Jäger, P.F., Wasserthal, J., Zimmerer, D., Petersen, J., Kohl, S., Schock, J., Klein, A., Roß, T., Wirkert, S., Neher, P., Dinkelacker, S., Köhler, G., Maier-Hein, K.H. (2020). batchgenerators - a python framework for data augmentation. Available at https://github.com/MIC-DKFZ/batchgenerators . doi:10.5281/zenodo.3632567
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2020
Cited alongside, same era.
Campello, Víctor M. et al.: Multi-Centre, Multi-Vendor & \& Multi-Disease Cardiac Image Segmentation. In preparation
Cited in the paper.