FedHarmony: unlearning scanner bias with distributed data

FedHarmony: unlearning scanner bias with distributed data

The ability to combine data across scanners and studies is vital for neuroimaging, to increase both statistical power and the representation of biological variability. However, combining datasets across sites leads to two challenges: first, an increase in undesirable non-biological variance due to s...

書目詳細資料
Main Authors:	Dinsdale, N, Jenkinson, M, Namburete, A
格式:	Conference item
語言:	English
出版:	Springer 2022

相似書籍

Unlearning scanner bias for MRI harmonisation
由: Dinsdale, NK, et al.
出版: (2020)

Unlearning scanner bias for MRI harmonisation in medical image segmentation
由: Dinsdale, NK, et al.
出版: (2020)

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal.
由: Dinsdale, NK, et al.
出版: (2020)

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal
由: Nicola K. Dinsdale, et al.
出版: (2021-03-01)

SFHarmony: source free domain adaptation for distributed neuroimaging analysis
由: Dinsdale, NK, et al.
出版: (2024)

STAMP: Simultaneous Training and Model Pruning for low data regimes in medical image segmentation
由: Dinsdale, NK, et al.
出版: (2022)

Spatial warping network for 3D segmentation of the hippocampus in MR images
由: Dinsdale, N, et al.
出版: (2019)

Anatomically plausible segmentations: explicitly preserving topology through prior deformations
由: Wyburd, MK, et al.
出版: (2024)

TEDS-Net: enforcing diffeomorphisms in spatial transformers to guarantee topology preservation in segmentations
由: Wyburd, MK, et al.
出版: (2021)

Challenges for machine learning in clinical translation of big data imaging studies
由: Dinsdale, NK, et al.
出版: (2022)

Prototype learning for explainable brain age prediction
由: Hesse, LS, et al.
出版: (2024)

Commentary: Unlearning implicit social biases during sleep
由: Balazs eAczel, et al.
出版: (2015-09-01)

Data protection with unlearnable examples
由: Ma, Xiaoyu
出版: (2024)

Learning patterns of the ageing brain in MRI using deep convolutional networks
由: Dinsdale, NK, et al.
出版: (2020)

The institute of unlearning
由: Ong, Jolyn Huixian
出版: (2016)

Unlearnable phonotactics
由: Arild Hestvik, et al.
出版: (2020-06-01)

UNLEARNED LESSONS
由: R.S.GRINBERG
出版: (2009-06-01)

Unlearning the Modern
由: David Elliott, et al.
出版: (2019-09-01)

Corrective machine unlearning
由: Goel, S, et al.
出版: (2024)

Ethnic discrimination unlearned: experience in the repeated Trust Game reduces trust bias
由: Caitlin Duncan, et al.
出版: (2023-05-01)

Learning, unlearning and relearning
由: Abd Razak, Dzulkifli
出版: (2009)

Coded Machine Unlearning
由: Nasser Aldaghri, et al.
出版: (2021-01-01)

Learning and Unlearning of Pain
由: Larissa Cordier, et al.
出版: (2018-06-01)

Review of Machine Unlearning
由: HE Lisong, YANG Yang
出版: (2024-11-01)

FedCIO: efficient exact federated unlearning with clustering, isolation, and one-shot aggregation
由: Qiu, Hongyu, et al.
出版: (2024)

The spacing principle for unlearning abnormal neuronal synchrony.
由: Oleksandr V Popovych, et al.
出版: (2015-01-01)

UNLEARN, RELEARN, LEARN AGAIN
由: Datta, Shoumen
出版: (2010)

Unlearnable example with face images
由: Peng, Haohang
出版: (2025)

Unlearning regularization for Boltzmann machines
由: Enrico Ventura, et al.
出版: (2024-01-01)

Learning and unlearning voicing assimilation
由: Zsuzsanna Bárkányi, et al.
出版: (2024-01-01)

Potential History: Unlearning Imperialism
由: Bianca van Laun
出版: (2020-11-01)

Unlearning the colonial cultures of planning /
由: Porter, Libby, 1973-
出版: (c201)

Unlearned lessons of the twentieth century
由: Pavlo Yaumchuk
出版: (2019-10-01)

Learning patterns of the ageing brain in MRI using deep convolutional networks
由: Nicola K. Dinsdale, et al.
出版: (2021-01-01)

Omni-supervised domain adversarial training for white matter hyperintensity segmentation in the UK Biobank
由: Sundaresan, V, et al.
出版: (2022)

“An Unlearned Antinomian-Anabaptist”: Richard Baxter on John Bunyan
由: N. H. Keeble
出版: (2019-07-01)

Understanding the molecular pharmacology of vascular calcium-activated chloride channels
由: Dinsdale, R
出版: (2021)

Optimising convolutional neural networks for large-scale neuroimaging studies
由: Dinsdale, NK
出版: (2021)

Private Data Protection with Machine Unlearning in Contrastive Learning Networks
由: Kongyang Chen, et al.
出版: (2024-12-01)

The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects
由: Balboni, E, et al.
出版: (2022)