STAMP: Simultaneous Training and Model Pruning for low data regimes in medical image segmentation

STAMP: Simultaneous Training and Model Pruning for low data regimes in medical image segmentation

Acquisition of high quality manual annotations is vital for the development of segmentation algorithms. However, to create them we require a substantial amount of expert time and knowledge. Large numbers of labels are required to train convolutional neural networks due to the vast number of paramete...

Fuld beskrivelse

Bibliografiske detaljer
Main Authors:	Dinsdale, NK, Jenkinson, M, Namburete, AIL
Format:	Journal article
Sprog:	English
Udgivet:	Elsevier 2022

Lignende værker

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

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

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

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

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

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

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

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

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

Cortical plate segmentation using CNNs in 3D fetal ultrasound
af: Wyburd, MK, et al.
Udgivet: (2020)

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

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

FedHarmony: unlearning scanner bias with distributed data
af: Dinsdale, N, et al.
Udgivet: (2022)

Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images
af: Sundaresan, V, et al.
Udgivet: (2021)

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

Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning
af: Hesse, LS, et al.
Udgivet: (2022)

Multi-task CNN for structural semantic segmentation in 3D fetal brain ultrasound
af: Venturini, L, et al.
Udgivet: (2020)

Computational methods for quantifying in vivo muscle fascicle curvature from ultrasound images
af: Namburete, AIL, et al.
Udgivet: (2011)

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

Self-supervised ultrasound to MRI fetal brain image synthesis
af: Jiao, J, et al.
Udgivet: (2020)

Anatomy-aware self-supervised fetal MRI synthesis from unpaired ultrasound images
af: Jiao, J, et al.
Udgivet: (2019)

Is your style transfer doing anything useful? an investigation into hippocampus segmentation and the role of preprocessing
af: Kalabizadeh, H, et al.
Udgivet: (2024)

Uncertainty estimates as data selection criteria to boost omni-supervised learning
af: Venturini, L, et al.
Udgivet: (2020)

CAP : Context-aware Pruning for semantic segmentation
af: He, Wei, et al.
Udgivet: (2021)

Assessment of regional cortical development through fissure based gestational age estimation in 3D fetal ultrasound
af: Wyburd, MK, et al.
Udgivet: (2021)

PruneFaceDet: Pruning lightweight face detection network by sparsity training
af: Nanfei Jiang, et al.
Udgivet: (2022-12-01)

loss for low-contrast medical image segmentation
af: Yu Zhao, et al.
Udgivet: (2024-01-01)

Safety in Hospital Medication Administration Applying STAMP Processes
af: Baker, Elizabeth White
Udgivet: (2022)

Weight pruning-UNet: Weight pruning UNet with depth-wise separable convolutions for semantic segmentation of kidney tumors
af: Patike Kiran Rao, et al.
Udgivet: (2022-01-01)

Compact Image-Style Transfer: Channel Pruning on the Single Training of a Network
af: Minseong Kim, et al.
Udgivet: (2022-11-01)

Simultaneous Kidney Transplantation and Abdominoplasty in a Prune-Belly Syndrome Patient
af: André Barcelos da Silva, et al.
Udgivet: (2023-09-01)

BASIC stamp /
af: Kuhnel, Claus, 1951-, et al.
Udgivet: (2000)

Learning to map 2D ultrasound images into 3D space with minimal human annotation
af: Yeung, P-H, et al.
Udgivet: (2021)

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

SinGAN-Seg: Synthetic training data generation for medical image segmentation
af: Vajira Thambawita, et al.
Udgivet: (2022-01-01)

SinGAN-Seg: Synthetic training data generation for medical image segmentation.
af: Vajira Thambawita, et al.
Udgivet: (2022-01-01)

Training and Pruning Florida Peaches, Nectarines, and Plums
af: Ali Sarkhosh, et al.
Udgivet: (2018-09-01)

Training and Pruning Florida Peaches, Nectarines, and Plums
af: James Ferguson
Udgivet: (2007-08-01)

Training and Pruning Florida Peaches, Nectarines, and Plums
af: Ali Sarkhosh, et al.
Udgivet: (2018-09-01)

Training and Pruning Florida Peaches, Nectarines, and Plums
af: James Ferguson
Udgivet: (2007-08-01)