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[HTML][HTML] Learning disentangled representations in the imaging domain
Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …
general representations even in the absence of, or with limited, supervision. A good general …
A review of predictive and contrastive self-supervised learning for medical images
Over the last decade, supervised deep learning on manually annotated big data has been
progressing significantly on computer vision tasks. But, the application of deep learning in …
progressing significantly on computer vision tasks. But, the application of deep learning in …
Deep learning for breast mri style transfer with limited training data
In this work we introduce a novel medical image style transfer method, StyleMapper, that can
transfer medical scans to an unseen style with access to limited training data. This is made …
transfer medical scans to an unseen style with access to limited training data. This is made …
Representation disentanglement for multi-modal brain MRI analysis
Multi-modal MRIs are widely used in neuroimaging applications since different MR
sequences provide complementary information about brain structures. Recent works have …
sequences provide complementary information about brain structures. Recent works have …
Breath-hold CBCT-guided CBCT-to-CT synthesis via multimodal unsupervised representation disentanglement learning
Adaptive radiation therapy (ART) aims to deliver radiotherapy accurately and precisely in the
presence of anatomical changes, in which the synthesis of computed tomography (CT) from …
presence of anatomical changes, in which the synthesis of computed tomography (CT) from …
Siamese semi-disentanglement network for robust PET-CT segmentation
Abstract A robust PET-CT segmentation network should guarantee that models trained on
the PET-CT images will still work when only CT images are available. It is particularly …
the PET-CT images will still work when only CT images are available. It is particularly …
Unseen domain generalization for prostate MRI segmentation via disentangled representations
In clinical practice, medical images obtained from different sites often exhibit appearance
variations, resulting in limited generalizability of deep learning models for segmentation in …
variations, resulting in limited generalizability of deep learning models for segmentation in …
Disentangled Representation of Longitudinal Β-Amyloid for AD Via Sequential Graph Variational Autoencoder with Supervision
The emergence of Positron Emission Tomography (PET) imaging allows us to quantify the
burden of amyloid plaques in-vivo, which is one of the hallmarks of Alzheimer's disease …
burden of amyloid plaques in-vivo, which is one of the hallmarks of Alzheimer's disease …
Combining multi-task learning and multi-channel variational auto-encoders to exploit datasets with missing observations-application to multi-modal neuroimaging …
L Antelmi, N Ayache, P Robert, F Ribaldi, V Garibotto… - 2021 - inria.hal.science
The joint modeling of neuroimaging data across multiple datasets requires to consistently
analyze high-dimensional and heterogeneous information in presence of often non …
analyze high-dimensional and heterogeneous information in presence of often non …
[كتاب][B] Multi-Dimensional Neuroimage Analysis
J Ouyang - 2024 - search.proquest.com
Multi-modal and longitudinal neuroimages (aka multi-dimensional neuroimages) are critical
for the understanding, diagnosis, and monitoring of neurological disorders. The complex …
for the understanding, diagnosis, and monitoring of neurological disorders. The complex …