Lifelong nnU-Net: a framework for standardized medical continual learning
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A. Mukhopadhyay | Camila González | Amin Ranem | Ahmed Othman | Daniel Pinto dos Santos | D. Pinto dos Santos | Daniel Pinto dos Santos
[1] M. Ghassemi,et al. The medical algorithmic audit. , 2022, The Lancet. Digital health.
[2] Oleg S. Pianykh,et al. Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging , 2021, Nature Communications.
[3] Kerstin N. Vokinger,et al. Regulating AI in medicine in the United States and Europe , 2021, Nature Machine Intelligence.
[4] Anirban Mukhopadhyay,et al. Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation , 2021, DART/FAIR@MICCAI.
[5] Anirban Mukhopadhyay,et al. Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation , 2021, MICCAI.
[6] Sergio Escalera,et al. Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge , 2021, IEEE Transactions on Medical Imaging.
[7] A. Kesselheim,et al. Continual learning in medical devices: FDA's action plan and beyond. , 2021, The Lancet. Digital health.
[8] Tinne Tuytelaars,et al. Rehearsal revealed: The limits and merits of revisiting samples in continual learning , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[9] Simone Calderara,et al. Avalanche: an End-to-End Library for Continual Learning , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[10] Jens Petersen,et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation , 2020, Nature Methods.
[11] A. Mukhopadhyay,et al. What is Wrong with Continual Learning in Medical Image Segmentation? , 2020, ArXiv.
[12] Visvanathan Ramesh,et al. A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning , 2020, Neural Networks.
[13] Philip H. S. Torr,et al. GDumb: A Simple Approach that Questions Our Progress in Continual Learning , 2020, ECCV.
[14] Spyridon Bakas,et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data , 2020, Scientific Reports.
[15] Aaron Y. Lee,et al. Clinical applications of continual learning machine learning. , 2020, The Lancet. Digital health.
[16] Micah J. Sheller,et al. The future of digital health with federated learning , 2020, npj Digital Medicine.
[17] Lequan Yu,et al. MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data , 2020, IEEE Transactions on Medical Imaging.
[18] B. Caputo,et al. Modeling the Background for Incremental Learning in Semantic Segmentation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Tae Joon Jun,et al. Dissecting Catastrophic Forgetting in Continual Learning by Deep Visualization , 2020, ArXiv.
[20] Keiji Yanai,et al. Continual Learning of Image Translation Networks Using Task-Dependent Weight Selection Masks , 2019, ACPR.
[21] Yuanyuan Wang,et al. The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN , 2019, MICCAI.
[22] Pietro Zanuttigh,et al. Incremental Learning Techniques for Semantic Segmentation , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[23] Ronald M. Summers,et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms , 2019, ArXiv.
[24] Konstantinos Kamnitsas,et al. Towards continual learning in medical imaging , 2018, ArXiv.
[25] David Filliat,et al. Don't forget, there is more than forgetting: new metrics for Continual Learning , 2018, ArXiv.
[26] Yen-Cheng Liu,et al. Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines , 2018, ArXiv.
[27] Philip H. S. Torr,et al. Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence , 2018, ECCV.
[28] Andrei A. Rusu,et al. Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.
[29] Tinne Tuytelaars,et al. Expert Gate: Lifelong Learning with a Network of Experts , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Guido Gerig,et al. ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[31] Derek Hoiem,et al. Learning without Forgetting , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[32] Andrea Bernasconi,et al. Multi-contrast submillimetric 3 Tesla hippocampal subfield segmentation protocol and dataset , 2015, Scientific Data.
[33] Guillaume Lemaitre,et al. Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review , 2015, Comput. Biol. Medicine.
[34] D. Louis Collins,et al. Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol , 2015, Alzheimer's & Dementia.
[35] Florian Jung,et al. Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge , 2014, Medical Image Anal..
[36] Chris W. Johnson,et al. Identifying common problems in the acquisition and deployment of large-scale, safety―critical, software projects in the US and UK healthcare systems , 2011 .
[37] Dwarikanath Mahapatra,et al. Continual Domain Incremental Learning for Chest X-Ray Classification in Low-Resource Clinical Settings , 2021, DART/FAIR@MICCAI.
[38] Robert C. Wolpert,et al. A Review of the , 1985 .