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Processing of medical images such as MRI or CT presents unique challenges compared to RGB images typically used in computer vision.
S. Ioffe, C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , in: International Conference on Machine Learning, PMLR, 2015, pp. 448–456, iSSN: 1938-7228. URL http://proceedings.mlr.press/v37/ioffe15.html
1938
Earlier work this paper cites.
doi:10.1097/00004728-199803000-00032
C. J. Holmes, R. Hoge, L. Collins, R. Woods, A. W. Toga, A. C. Evans, Enhancement of MR images using registration for signal averaging, Journal of Computer Assisted Tomography 22 (2) (1998) 324–333 · 1998
Earlier work this paper cites.
3.0.co;2-m" title="" class="ltx_ref ltx_href"> doi:10.1002/(sici)1522-2594(199912)42:6<1072::aid-mrm11>3.0.co;2-m
L. G. Nyúl, J. K. Udupa, On standardizing the MR image intensity scale, Magnetic Resonance in Medicine 42 (6) (1999) 1072–1081 · 1999
Earlier work this paper cites.
doi:10.1109/42.811270
K. Van Leemput, F. Maes, D. Vandermeulen, P. Suetens, Automated model-based tissue classification of MR images of the brain, IEEE transactions on medical imaging 18 (10) (1999) 897–908 · 1999
Earlier work this paper cites.
doi:10.1109/42.836373
L. G. Nyúl, J. K. Udupa, X. Zhang, New variants of a method of MRI scale standardization, IEEE transactions on medical imaging 19 (2) (2000) 143–150 · 2000
Earlier work this paper cites.
2004
Earlier work this paper cites.
doi:10.1148/rg.261055134
J. Zhuo, R. P. Gullapalli, MR Artifacts, Safety, and Quality Control , RadioGraphics 26 (1) (2006) 275–297 · 2006
Earlier work this paper cites.
doi:10.1109/CVPR.2009.5206848
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, ImageNet: A large-scale hierarchical image database, in: 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248–255, iSSN: 1063-6919 · 2009
Earlier work this paper cites.
doi:10.1109/MCSE.2011.37
S. van der Walt, S. C. Colbert, G. Varoquaux, The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science Engineering 13 (2) (2011) 22–30, conference Name: Computing in Science Engineering · 2011
Earlier work this paper cites.
doi:10.1016/j.mri.2012.05.001
A. Fedorov, R. Beichel, J. Kalpathy-Cramer, J. Finet, J.-C. Fillion-Robin, S. Pujol, C. Bauer, D. Jennings, F. Fennessy, M. Sonka, J. Buatti, S. Aylward, J. V. Miller, S. Pieper, R. Kikinis, 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network, Magnetic resonance imaging 30 (9) (2012) 1323–1341 · 2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, in: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, NIPS’12, Curran Associates Inc., USA, 2012, pp. 1097–1105
2012
Earlier work this paper cites.
doi:10.3389/fninf.2013.00045
B. C. Lowekamp, D. T. Chen, L. Ibáñez, D. Blezek, The Design of SimpleITK, Frontiers in Neuroinformatics 7 (2013) 45 · 2013
Earlier work this paper cites.
doi:10.1007/s10278-013-9657-9
M. Larobina, L. Murino, Medical Image File Formats , Journal of Digital Imaging 27 (2) (2014) 200–206 · 2014
Earlier work this paper cites.
doi:10.1109/TMI.2015.2418298
M. J. Cardoso, M. Modat, R. Wolz, A. Melbourne, D. Cash, D. Rueckert, S. Ourselin, Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion, IEEE transactions on medical imaging 34 (9) (2015) 1976–1988 · 2015
Earlier work this paper cites.
F. Chollet, others, Keras , 2015. URL https://keras.io
2015
Earlier work this paper cites.
doi:10.1007/978-3-319-46723-8_49
O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, O. Ronneberger, 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation, in: S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, W. Wells (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, Lecture Notes in Computer Science, Springer International Publishing, Cham, 2016, pp. 424–432 · 2016
Earlier work this paper cites.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng, TensorFlow: a system for large-scale machine learning, in: Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation, OSDI’16, USENIX Association, USA, 2016, pp. 265–283
2016
Earlier work this paper cites.
doi:10.5281/zenodo.44297
wiredfool, A. Clark, Hugo, A. Murray, A. Karpinsky, C. Gohlke, B. Crowell, D. Schmidt, A. Houghton, S. Johnson, S. Mani, J. Ware, D. Caro, S. Kossouho, E. W. Brown, A. Lee, M. Korobov, M. Górny, E. S. Santana, N. Pieuchot, O. Tonnhofer, M. Brown, B. Pierre, J. C. Abela, L. J. Solberg, F. Reyes, A. Buzanov, Y. Yu, eliempje, F. Tolf, Pillow: 3.1.0 (Jan. 2016) · 2016
Earlier work this paper cites.
doi:10.1109/3DV.2016.79
F. Milletari, N. Navab, S.-A. Ahmadi, V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation, in: 2016 Fourth International Conference on 3D Vision (3DV), 2016, pp. 565–571 · 2016
Earlier work this paper cites.
doi:10.1186/s40537-016-0043-6
K. Weiss, T. M. Khoshgoftaar, D. Wang, A survey of transfer learning , Journal of Big Data 3 (1) (2016) 9 · 2016
Earlier work this paper cites.
2017
Cited alongside, same era.
doi:10.1007/978-3-319-59050-9_28
W. Li, G. Wang, L. Fidon, S. Ourselin, M. J. Cardoso, T. Vercauteren, On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task, in: M. Niethammer, M. Styner, S. Aylward, H. Zhu, I. Oguz, P.-T. Yap, D. Shen (Eds.), Information Processing in Medical Imaging, Lecture Notes in Computer Science, Springer International Publishing, Cham, 2017, pp. 348–360 · 2017
Cited alongside, same era.
doi:10.1016/j.media.2017.02.007
C. H. Sudre, M. J. Cardoso, S. Ourselin, Longitudinal segmentation of age-related white matter hyperintensities, Medical Image Analysis 38 (2017) 50–64 · 2017
Cited alongside, same era.
doi:10.1038/s41598-018-22871-z
D. Lu, K. Popuri, G. W. Ding, R. Balachandar, M. F. Beg, Alzheimer’s Disease Neuroimaging Initiative, Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images, Scientific Reports 8 (1) (2018) 5697 · 2018
Cited alongside, same era.
doi:10.1007/978-3-030-32245-8_38
M. C. H. Lee, O. Oktay, A. Schuh, M. Schaap, B. Glocker, Image-and-Spatial Transformer Networks for Structure-Guided Image Registration, in: D. Shen, T. Liu, T. M. Peters, L. H. Staib, C. Essert, S. Zhou, P.-T. Yap, A. Khan (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, Lecture Notes in Computer Science, Springer International Publishing, Cham, 2019, pp. 337–345 · 2019
Later among the works it cites.
A. B. Jung, K. Wada, J. Crall, S. Tanaka, J. Graving, C. Reinders, S. Yadav, J. Banerjee, G. Vecsei, A. Kraft, Z. Rui, J. Borovec, C. Vallentin, S. Zhydenko, K. Pfeiffer, B. Cook, I. Fernández, F.-M. De Rainville, C.-H. Weng, A. Ayala-Acevedo, R. Meudec, M. Laporte, others, imgaug (2020). URL https://github.com/aleju/imgaug
2020
Closest in time.
T. Chen, S. Kornblith, M. Norouzi, G. Hinton, A Simple Framework for Contrastive Learning of Visual Representations , in: International Conference on Machine Learning, PMLR, 2020, pp. 1597–1607, iSSN: 2640-3498. URL http://proceedings.mlr.press/v119/chen20j.html
2020
Closest in time.
doi:10.5281/zenodo.3632567
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F. Chen, V. Taviani, I. Malkiel, J. Y. Cheng, J. I. Tamir, J. Shaikh, S. T. Chang, C. J. Hardy, J. M. Pauly, S. S. Vasanawala, Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks , Radiology 289 (2) (2018) 366–373 · 2018
Cited alongside, same era.
2018
Cited alongside, same era.
doi:10.1016/j.cmpb.2018.01.025
E. Gibson, W. Li, C. Sudre, L. Fidon, D. I. Shakir, G. Wang, Z. Eaton-Rosen, R. Gray, T. Doel, Y. Hu, T. Whyntie, P. Nachev, M. Modat, D. C. Barratt, S. Ourselin, M. J. Cardoso, T. Vercauteren, NiftyNet: a deep-learning platform for medical imaging , Computer Methods and Programs in Biomedicine 158 (2018) 113–122 · 2018
Cited alongside, same era.
2018
Cited alongside, same era.
doi:10.1016/j.cobme.2018.12.005
V. Cheplygina, Cats or CAT scans: Transfer learning from natural or medical image source data sets? , Current Opinion in Biomedical Engineering 9 (2019) 21–27 · 2018
Cited alongside, same era.
doi:10.1109/WACV.2018.00066
V. V. Valindria, N. Pawlowski, M. Rajchl, I. Lavdas, E. O. Aboagye, A. G. Rockall, D. Rueckert, B. Glocker, Multi-modal Learning from Unpaired Images: Application to Multi-organ Segmentation in CT and MRI, in: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018, pp. 547–556 · 2018
Cited alongside, same era.
doi:10.5281/zenodo.1495335
C. S. Perone, cclauss, E. Saravia, P. L. Ballester, MohitTare, perone/medicaltorch: Release v0.2 (Nov. 2018) · 2018
Cited alongside, same era.
doi:10.1007/978-3-319-95921-4_26
L. Berger, H. Eoin, M. J. Cardoso, S. Ourselin, An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation, in: M. Nixon, S. Mahmoodi, R. Zwiggelaar (Eds.), Medical Image Understanding and Analysis, Communications in Computer and Information Science, Springer International Publishing, Cham, 2018, pp. 277–286 · 2018
Cited alongside, same era.
F. Isensee, P. Jäger, J. Wasserthal, D. Zimmerer, J. Petersen, S. Kohl, J. Schock, A. Klein, T. Roß, S. Wirkert, P. Neher, S. Dinkelacker, G. Köhler, K. Maier-Hein, batchgenerators - a python framework for data augmentation (Jan. 2020) · 2020
Closest in time.
P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, I. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, SciPy 1.0: fundamental algorithms for scientific computing in Python , Nature Methods (2020) 1–12 doi:10.1038/s41592-019-0686-2
2020
Closest in time.
doi:10.1016/j.cmpb.2020.105796
A. Jungo, O. Scheidegger, M. Reyes, F. Balsiger, pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis , Computer Methods and Programs in Biomedicine 198 (2021) 105796 · 2020
Closest in time.
R. Shaw, C. H. Sudre, S. Ourselin, M. J. Cardoso, A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality , in: Medical Imaging with Deep Learning, PMLR, 2020, pp. 733–742, iSSN: 2640-3498. URL http://proceedings.mlr.press/v121/shaw20a.html
2020
Closest in time.
B. Billot, D. N. Greve, K. V. Leemput, B. Fischl, J. E. Iglesias, A. Dalca, A Learning Strategy for Contrast-agnostic MRI Segmentation, in: Medical Imaging with Deep Learning, PMLR, 2020, pp. 75–93, iSSN: 2640-3498
2020
Closest in time.
doi:10.1007/978-3-030-59728-3_18
B. Billot, E. Robinson, A. V. Dalca, J. E. Iglesias, Partial Volume Segmentation of Brain MRI Scans of Any Resolution and Contrast, in: A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, L. Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Lecture Notes in Computer Science, Springer International Publishing, Cham, 2020, pp. 177–187 · 2020
Closest in time.
doi:10.1007/978-3-030-59716-0_12
F. Pérez-García, R. Rodionov, A. Alim-Marvasti, R. Sparks, J. S. Duncan, S. Ourselin, Simulation of Brain Resection for Cavity Segmentation Using Self-supervised and Semi-supervised Learning, in: A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, L. Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Lecture Notes in Computer Science, Springer International Publishing, Cham, 2020, pp. 115–125 · 2020
Closest in time.
2020
Closest in time.
doi:10.1038/s41598-020-61808-3
N. Moshkov, B. Mathe, A. Kertesz-Farkas, R. Hollandi, P. Horvath, Test-time augmentation for deep learning-based cell segmentation on microscopy images , Scientific Reports 10 (1) (2020) 5068, number: 1 Publisher: Nature Publishing Group · 2020
Closest in time.
T. Preston-Werner, Semantic Versioning 2.0.0 , library Catalog: semver.org (2020). URL https://semver.org/
2020
Closest in time.
doi:10.5281/zenodo.4296288
F. Pérez-García, fepegar/torchio: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (Nov. 2020) · 2020
Closest in time.
doi:10.1038/s41592-020-01008-z
F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, K. H. Maier-Hein, nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation , Nature Methods 18 (2) (2021) 203–211, number: 2 Publisher: Nature Publishing Group · 2021
Closest in time.
2021
Closest in time.
doi:10.5281/zenodo.4679866
N. Ma, W. Li, R. Brown, Y. Wang, B. Gorman, Behrooz, H. Johnson, I. Yang, E. Kerfoot, Y. Li, M. Adil, Y.-T. Hsieh, charliebudd, A. Aggarwal, C. Trentz, adam aji, B. Murray, G. Daroach, P.-D. Tudosiu, myron, M. Graham, Balamurali, C. Baker, J. Sellner, L. Fidon, A. Powers, G. Leroy, Alxaline, D. Schulz, Project-MONAI/MONAI: 0.5.0 (Apr. 2021) · 2021
Closest in time.
2021
Closest in time.
M. McCormick, D. Zukić, S. A. on, ITK 5.2 Release Candidate 3 available for testing (Mar. 2021). URL https://blog.kitware.com/itk-5-2-release-candidate-3-available-for-testing/
2021
Closest in time.
doi:10.3390/info11020125
A. Buslaev, V. I. Iglovikov, E. Khvedchenya, A. Parinov, M. Druzhinin, A. A. Kalinin, Albumentations: Fast and Flexible Image Augmentations , Information 11 (2) (2020) 125, number: 2 Publisher: Multidisciplinary Digital Publishing Institute · 2078
Closest in time.