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Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains.
Joint Segmentation Of Multiple Sclerosis Lesions And Brain Anatomy In MRI Scans Of Any Contrast And Resolution With CNNs, in: IEEE International Symposium on Biomedical Imaging, pp. 1971–1974
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Statistical Power Analysis for the Behavioural Sciences
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Partial volume tissue classification of multichannel magnetic resonance images-a mixel model
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Corpus Callosum Morphology in Attention Deficit-Hyperactivity Disorder: Morphometric Analysis of MRI
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Multi-modal volume registration by maximization of mutual information
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Comparison and Evaluation of Retrospective Intermodality Brain Image Registration Techniques
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Automated model-based tissue classification of MR images of the brain
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Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain
Fischl, B., Salat, D., Busa, E., Albert, M., et al., 2002 · 2002
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A unifying framework for partial volume segmentation of brain MR images
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P., 2003 · 2003
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Sequence-independent segmentation of magnetic resonance images
Fischl, B., Salat, D., van der Kouwe, A., Makris, N., Ségonne, F., Quinn, B., Dale, A., 2004 · 2004
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Simultaneous truth and performance level estimation: an algorithm for the validation of image segmentation
Warfield, S., Zou, K., Wells, W., 2004 · 2004
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Unified segmentation
Ashburner, J., Friston, K., 2005 · 2005
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A log-Euclidean framework for statistics on diffeomorphisms, in: Medical Image Computing and Computer Assisted Intervention, pp. 924–931
Arsigny, V., Commowick, O., Pennec, X., Ayache, N., 2006 · 2006
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Open Access Series of Imaging Studies: Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults
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Validation of hippocampal volumes measured using a manual method and two automated methods (FreeSurfer and IBASPM) in chronic major depressive disorder
Tae, W., Kim, S., Lee, K., Nam, E., Kim, K., 2008 · 2008
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Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI
Chupin, M., Gérardin, E., Cuingnet, R., Boutet, C., Lemieux, L., Lehéricy, S., et al., 2009 · 2009
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A Survey on Transfer Learning
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Deep Learners Benefit More from Out-of-Distribution Examples, in: International Conference on Artificial Intelligence and Statistics, pp. 164–172
Bengio, Y., Bastien, F., Bergeron, A., Boulanger–Lewandowski, N., Breuel, T., Chherawala, Y., Cisse, M., et al., 2011 · 2011
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The Parkinson Progression Marker Initiative (PPMI)
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Underconnected, but How? A Survey of Functional Connectivity MRI Studies in Autism Spectrum Disorders
Müller, R., Shih, P., Keehn, B., Deyoe, J., Leyden, K., Shukla, D., 2011 · 2011
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The ADHD-200 Consortium: A Model to Advance the Translational Potential of Neuroimaging in Clinical Neuroscience
Consortium, T.A.., 2012 · 2012
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FreeSurfer
Fischl, B., 2012 · 2012
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The Human Connectome Project: A data acquisition perspective
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The MCIC collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia
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The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism
Di Martino, A., Yan, C.G., Li, Q., Denio, E., Castellanos, F., et al., 2014 · 2014
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Synthetic data augmentation using gan for improved liver lesion classification, in: IEEE International Symposium on Biomedical Imaging, pp. 289–293
Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., Greenspan, H., 2018 · 2018
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A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology
Iglesias, J.E., Insausti, R., Lerma-Usabiaga, G., Bocchetta, M., Van Leemput, K., Greve, D., 2018 · 2018
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Pulse Sequence Resilient Fast Brain Segmentation, in: Medical Image Computing and Computer Assisted Intervention, pp. 654–662
Jog, A., Fischl, B., 2018 · 2018
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A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols, in: Medical Image Computing and Computer Assisted Intervention, pp. 476–484
Karani, N., Chaitanya, K., Baumgartner, C., Konukoglu, E., 2018 · 2018
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Training Deep Networks With Synthetic Data: Bridging the Reality Gap by Domain Randomization, in: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 969–977
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Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures
Holmes, A., Hollinshead, M., O’Keefe, T., Petrov, V., Fariello, G., et al., 2015 · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, in: International Conference on Machine Learning, pp. 448–456
Ioffe, S., Szegedy, C., 2015 · 2015
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U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Medical Image Computing and Computer-Assisted Intervention, pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
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Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets
Tobon-Gomez, C., Geers, A.J., Peters, J., 2015 · 2015
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Tensorflow: A system for large-scale machine learning, in: Symposium on Operating Systems Design and Implementation, pp. 265–283
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., 2016 · 2016
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Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.A., Unterthiner, T., Hochreiter, S., 2016 · 2016
Cited alongside, same era.
HeMIS: Hetero-Modal Image Segmentation, in: Medical Image Computing and Computer-Assisted Intervention, pp. 469–477
Havaei, M., Guizard, N., Chapados, N., Bengio, Y., 2016 · 2016
Cited alongside, same era.
Tremblay, J., Prakash, A., Acuna, D., Brophy, M., Jampani, V., Anil, C., To, T., Cameracci, E., Boochoon, S., Birchfield, S., 2018 · 2018
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Translating and Segmenting Multimodal Medical Volumes With Cycle- and Shape-Consistency Generative Adversarial Network, in: IEEE Conference on Computer Vision and Pattern Recognition, pp. 242–251
Zhang, Z., Yang, L., Zheng, Y., 2018 · 2018
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Semi-supervised and Task-Driven Data Augmentation, in: Information Processing in Medical Imaging, pp. 29–41
Chaitanya, K., Karani, N., Baumgartner, C., Becker, A., Donati, O., Konukoglu, E., 2019 · 2019
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Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation
Chen, C., Dou, Q., Chen, H., Qin, J., Heng, P.A., 2019 · 2019
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Pnp-adanet: Plug-and-play adversarial domain adaptation network at unpaired cross-modality cardiac segmentation
Dou, Q., Ouyang, C., Chen, C., Chen, H., Glocker, B., Zhuang, X., Heng, P.A., 2019 · 2019
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SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth
Huo, Y., Xu, Z., Moon, H., Bao, S., Assad, A., Moyo, T., Savona, M., Abramson, R., Landman, B., 2019 · 2019
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Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks
Sandfort, V., Yan, K., Pickhardt, P., Summers, R., 2019 · 2019
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Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation, in: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8543–8553
Zhao, A., Balakrishnan, G., Durand, F., Guttag, J., Dalca, A., 2019 · 2019
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Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge
Zhuang, X., Li, L., Payer, C., 2019 · 2019
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Contrastive learning of global and local features for medical image segmentation with limited annotations, in: Advances in Neural Information Processing Systems, pp. 12546–12558
Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E., 2020 · 2020
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Deep adversarial training for multi-organ nuclei segmentation in histopathology images
Mahmood, F., Borders, D., Chen, R., Mckay, G., Salimian, K., Baras, A., Durr, N., 2020 · 2020
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Test-time augmentation for deep learning-based cell segmentation on microscopy images
Moshkov, N., Mathe, B., Kertesz-Farkas, A., Hollandi, R., Horvath, P., 2020 · 2020
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Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling
Puonti, O., Van Leemput, K., Saturnino, G., Siebner, H., Madsen, K., Thielscher, A., 2020 · 2020
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Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation
Zhang, L., Wang, X., Yang, D., Sanford, T., Harmon, S., Turkbey, B., Wood, B.J., Roth, H., Myronenko, A., Xu, D., Xu, Z., 2020 · 2020
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Autoencoder Based Self-Supervised Test-Time Adaptation for Medical Image Analysis
He, Y., Carass, A., Zuo, L., Dewey, B., Prince, J., 2021 · 2021
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Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast
Iglesias, J.E., Billot, B., Balbastre, Y., Tabari, A., Conklin, J., Gilberto González, R., Alexander, D.C., Golland, P., Edlow, B.L., Fischl, B., 2021 · 2021
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nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P., Kohl, S., Petersen, J., Maier-Hein, K., 2021 · 2021
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Test-time adaptable neural networks for robust medical image segmentation
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Enhancing mr image segmentation with realistic adversarial data augmentation
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