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Continual learning protocols are attracting increasing attention from the medical imaging community.
Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, M. & Cohen, N. J · 1989
Earlier work this paper cites.
Connectionist models of recognition memory: constraints imposed by learning and forgetting functions
Ratcliff, R · 1990
Earlier work this paper cites.
Neural networks with a self-refreshing memory: knowledge transfer in sequential learning tasks without catastrophic forgetting
Ans, B. & Rousset, S · 2000
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I. & Hinton, G. E · 2012
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Structural synaptic plasticity has high memory capacity and can explain graded amnesia, catastrophic forgetting, and the spacing effect
Knoblauch, A., Körner, E., Körner, U. & Sommer, F. T · 2014
Earlier work this paper cites.
Unsupervised neuron selection for mitigating catastrophic forgetting in neural networks
Goodrich, B. & Arel, I · 2014
Earlier work this paper cites.
Neural modularity helps organisms evolve to learn new skills without forgetting old skills
Ellefsen, K. O., Mouret, J.-B. & Clune, J · 2015
Earlier work this paper cites.
Knowledge transfer in deep block-modular neural networks
Terekhov, A. V., Montone, G. & O’Regan, J. K · 2015
Earlier work this paper cites.
Multi-contrast submillimetric 3 tesla hippocampal subfield segmentation protocol and dataset
Kulaga-Yoskovitz, J. et al · 2015
Earlier work this paper cites.
Cross-stitch networks for multi-task learning
Misra, I., Shrivastava, A., Gupta, A. & Hebert, M · 2016
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Rusu, A. A. et al · 2016
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Expert gate: Lifelong learning with a network of experts
Aljundi, R., Chakravarty, P. & Tuytelaars, T · 2017
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icarl: Incremental classifier and representation learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G. & Lampert, C. H · 2017
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Fearnet: Brain-inspired model for incremental learning
Kemker, R. & Kanan, C · 2017
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Neurogenesis deep learning: Extending deep networks to accommodate new classes
Draelos, T. J. et al · 2017
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Continual learning with deep generative replay
Shin, H., Lee, J. K., Kim, J. & Kim, J · 2017
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Learning without forgetting
Li, Z. & Hoiem, D · 2017
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Growing a brain: Fine-tuning by increasing model capacity
Wang, Y.-X., Ramanan, D. & Hebert, M · 2017
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Analyzing modular cnn architectures for joint depth prediction and semantic segmentation
Jafari, O. H., Groth, O., Kirillov, A., Yang, M. Y. & Rother, C · 2017
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J. et al · 2017
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Continual learning through synaptic intelligence
Zenke, F., Poole, B. & Ganguli, S · 2017
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Variational continual learning
Nguyen, C. V., Li, Y., Bui, T. D. & Turner, R. E · 2017
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Learning multiple visual domains with residual adapters
Rebuffi, S.-A., Bilen, H. & Vedaldi, A · 2017
Cited alongside, same era.
Incremental learning of object detectors without catastrophic forgetting
Shmelkov, K., Schmid, C. & Alahari, K · 2017
Cited alongside, same era.
A harmonized segmentation protocol for hippocampal and parahippocampal subregions: Why do we need one and what are the key goals?
Wisse, L. E. et al · 2017
Cited alongside, same era.
Federated learning of predictive models from federated electronic health records
Brisimi, T. S. et al · 2018
Cited alongside, same era.
Towards continual learning in medical imaging
Baweja, C., Glocker, B. & Kamnitsas, K · 2018
Cited alongside, same era.
Distributed weight consolidation: A brain segmentation case study
Continual learning via neural pruning
Golkar, S., Kagan, M. & Cho, K · 2019
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Uncertainty-guided continual learning with bayesian neural networks
Ebrahimi, S., Elhoseiny, M., Darrell, T. & Rohrbach, M · 2019
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Improving and understanding variational continual learning
Swaroop, S., Nguyen, C. V., Bui, T. D. & Turner, R. E · 2019
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Ace: Adapting to changing environments for semantic segmentation
Wu, Z., Wang, X., Gonzalez, J. E., Goldstein, T. & Davis, L. S · 2019
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Incremental learning techniques for semantic segmentation
Michieli, U. & Zanuttigh, P · 2019
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McClure, P. et al · 2018
Cited alongside, same era.
Learn the new, keep the old: Extending pretrained models with new anatomy and images
Ozdemir, F., Fuernstahl, P. & Goksel, O · 2018
Cited alongside, same era.
Keep and learn: Continual learning by constraining the latent space for knowledge preservation in neural networks
Kim, H.-E., Kim, S. & Lee, J · 2018
Cited alongside, same era.
A lifelong learning approach to brain mr segmentation across scanners and protocols
Karani, N., Chaitanya, K., Baumgartner, C. & Konukoglu, E · 2018
Cited alongside, same era.
Packnet: Adding multiple tasks to a single network by iterative pruning
Mallya, A. & Lazebnik, S · 2018
Cited alongside, same era.
Lifelong learning with dynamically expandable network
Yoon, J., Lee, J., Yang, E. & Hwang, S. J · 2018
Cited alongside, same era.
Memory aware synapses: Learning what (not) to forget
Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M. & Tuytelaars, T · 2018
Cited alongside, same era.
Continuous learning ai in radiology: implementation principles and early applications
Pianykh, O. S. et al · 2020
Closest in time.
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A. & Smith, V · 2020
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Continual learning for domain adaptation in chest x-ray classification
Lenga, M., Schulz, H. & Saalbach, A · 2020
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Continual learning of image translation networks using task-dependent weight selection masks
Matsumoto, A. & Yanai, K · 2020
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Importance driven continual learning for segmentation across domains
Özgün, S., Rickmann, A.-M., Roy, A. G. & Wachinger, C · 2020
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Continual class incremental learning for ct thoracic segmentation
Elskhawy, A. et al · 2020
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Dynamic memory to alleviate catastrophic forgetting in continuous learning settings
Hofmanninger, J. et al · 2020
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Dissecting catastrophic forgetting in continual learning by deep visualization
Nguyen, G. et al · 2020
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Adversarial continual learning
Ebrahimi, S., Meier, F., Calandra, R., Darrell, T. & Rohrbach, M · 2020
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Modeling the background for incremental learning in semantic segmentation
Cermelli, F., Mancini, M., Bulo, S. R., Ricci, E. & Caputo, B · 2020
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Ms-net: Multi-site network for improving prostate segmentation with heterogeneous mri data
Liu, Q., Dou, Q., Yu, L. & Heng, P. A · 2020
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nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J. & Maier-Hein, K. H · 2021
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Multi-centre, multi-vendor and multi-disease cardiac segmentation: The m&ms challenge
Campello, V. M. et al · 2021
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Three types of incremental learning
van de Ven, G. M., Tuytelaars, T. & Tolias, A. S · 2022
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The medical segmentation decathlon
Antonelli, M. et al · 2022
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Lifelong nnunet: a framework for standardized medical continual learning
Gonzalez, C., Ranem, A., dos Santos, D. P., Othman, A. & Mukhopadhyay, A · 2022
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Monai: An open-source framework for deep learning in healthcare, DOI: 10.48550/ARXIV.2211.02701 (2022)
Cardoso, M. J. et al · 2022
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