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Disentangling A Single MR Modality
[article]
2022
arXiv
pre-print
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Current methods learn disentangled representations using either paired multi-modal images with the same underlying anatomy or auxiliary labels (e.g., manual delineations) to provide inductive bias for ...
Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks. ...
Results show that our single-modal disentangling framework achieves performance comparable to methods which rely on multi-modal images for disentanglement. ...
arXiv:2205.04982v1
fatcat:srvmj4ad2vdpxcp2j7s5ymuy3e
Disentangled Representation Learning in Cardiac Image Analysis
[article]
2019
arXiv
pre-print
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We demonstrate that this high-level representation is ideally suited for several medical image analysis tasks, such as semi-supervised segmentation, multi-task segmentation and regression, and image-to-image ...
Here, we explicitly learn this decomposed (disentangled) representation of imaging data, focusing in particular on cardiac images. ...
Motivation Disentangled representations have considerable potential in the analysis of medical data. ...
arXiv:1903.09467v4
fatcat:lsdtpg2cove5thk4r35osw2gni
Disentangled representation learning in cardiac image analysis
2019
Medical Image Analysis
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We demonstrate that this high-level representation is ideally suited for several medical image analysis tasks, such as semi-supervised segmentation, multi-task segmentation and regression, and image-to-image ...
Here, we explicitly learn this decomposed (disentangled) representation of imaging data, focusing in particular on cardiac images. ...
Motivation Disentangled representations have considerable potential in the analysis of medical data. ...
doi:10.1016/j.media.2019.101535
pmid:31351230
pmcid:PMC6815716
fatcat:amrltox6svgk7oemhjivkyzfly
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
[article]
2020
arXiv
pre-print
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Taking advantage of the common information shared between modalities (an organ's anatomy) is beneficial for multi-modality processing and learning. ...
Despite advances in image analysis, we tend to treat each sequence, here termed modality, in isolation. ...
However, a disentangled representation with modality invariant anatomy factors is not enough for multimodal learning. ...
arXiv:1911.04417v4
fatcat:qxlay6fzz5fdlcpta2epygydf4
Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings
[article]
2019
arXiv
pre-print
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In this paper, we propose to enrich the embedding by disentangling parts-of-speech (PoS) in the accompanying captions. We build a separate multi-modal embedding space for each PoS tag. ...
We also demonstrate the benefit of disentangling the PoS for the generic task of cross-modal video retrieval on the MSR-VTT dataset. ...
We first disentangle a caption into its parts of speech (PoS) and learn a Multi-Modal Embedding Network (MMEN, Sec. 3.1) for each PoS (Sec. 3.2). ...
arXiv:1908.03477v1
fatcat:5po6jw4sczgzhezll4isf7x3my
Brain tumor segmentation with missing modalities via latent multi-source correlation representation
[article]
2020
arXiv
pre-print
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Multimodal MR images can provide complementary information for accurate brain tumor segmentation. However, it's common to have missing imaging modalities in clinical practice. ...
Since there exists a strong correlation between multi modalities, a novel correlation representation block is proposed to specially discover the latent multi-source correlation. ...
Modeling the latent multi-source correlation Inspired by a fact that, there is strong correlation between multi MR modalities, since the same scene (the same patient) is observed by different modalities ...
arXiv:2003.08870v4
fatcat:jlcwhpuzh5bjvnbes5xhyabvaa
Multi-task Shape Regression for Medical Image Segmentation
[chapter]
2016
Lecture Notes in Computer Science
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different applications irrespective of modalities. ...
In this paper, we propose a general segmentation framework of Multi-Task Shape Regression (MTSR) which formulates segmentation as multi-task learning to leverage its strength of jointly solving multiple ...
Introduction Segmentation plays a fundamental role in medical image analysis, which however has long been regarded as a challenging task due to great diversity of applications in multiple modalities, huge ...
doi:10.1007/978-3-319-46726-9_25
fatcat:kha2d3adxrb57c6y3rzr6hx22q
Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings
2019
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Preserved Fulltext
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In this paper, we propose to enrich the embedding by disentangling parts-of-speech (PoS) in the accompanying captions. We build a separate multi-modal embedding space for each PoS tag. ...
We also demonstrate the benefit of disentangling the PoS for the generic task of cross-modal video retrieval on the MSR-VTT dataset. ...
We first disentangle a caption into its parts of speech (PoS) and learn a Multi-Modal Embedding Network (MMEN, Sec. 3.1) for each PoS (Sec. 3.2). ...
doi:10.1109/iccv.2019.00054
dblp:conf/iccv/WrayCLD19
fatcat:7g5jonm73fegbhkuqrauidnz4a
Multi-Domain Image Completion for Random Missing Input Data
[article]
2020
arXiv
pre-print
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Specifically, we develop a novel multi-domain image completion method that utilizes a generative adversarial network (GAN) with a representational disentanglement scheme to extract shared skeleton encoding ...
We further illustrate that the learned representation in multi-domain image completion could be leveraged for high-level tasks, e.g., segmentation, by introducing a unified framework consisting of image ...
[39, 9, 3] also discuss how to extract representations from multi-modalities especially for segmentation with missing imaging modalities. ...
arXiv:2007.05534v1
fatcat:buih5jhb5javlla4mmz7v32eqm
Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework
[article]
2020
arXiv
pre-print
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Given their different artefact appearance, their complementary information can compensate for the corrupted signal in either modality. ...
We thus propose an unsupervised deep learning method for multimodal MAR. We introduce the use of Locally Normalised Cross Correlation as a loss term to encourage the fusion of multimodal information. ...
First, the ADN architecture was not modified, keeping the same amount of parameters for either single-or multi-modality tests and thus not optimising the model capacity to the multimodal task. ...
arXiv:2004.09321v1
fatcat:llgyf3nllzd4bc4dvvehh5m3ri
Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation
[article]
2022
arXiv
pre-print
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We conduct a series of experiments with two cross-modality adaptation tasks, i,e. cardiac substructure and abdominal multi-organ segmentation. ...
Unsupervised cross-modality medical image adaptation aims to alleviate the severe domain gap between different imaging modalities without using the target domain label. ...
Related Work Domain Adaptation in Medical Image Analysis. There has been a rapid development of deep learning models for medical image analysis [38, 42, 14, 15] . ...
arXiv:2205.11888v1
fatcat:a5z263udqbantdapyqnqmjvhse
Combining Multimodal Information for Metal Artefact Reduction: An Unsupervised Deep Learning Framework
2020
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
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Given their different artefact appearance, their complementary information can compensate for the corrupted signal in either modality. ...
We thus propose an unsupervised deep learning method for multimodal MAR. We introduce the use of Locally Normalised Cross Correlation as a loss term to encourage the fusion of multimodal information. ...
First, the ADN architecture was not modified, keeping the same amount of parameters for either single-or multi-modality tests and thus not optimising the model capacity to the multimodal task. ...
doi:10.1109/isbi45749.2020.9098633
dblp:conf/isbi/RanziniGKCHOHM20
fatcat:fkw5hl2a2bcjha6gqcl4g6xqui
Review of Disentanglement Approaches for Medical Applications – Towards Solving the Gordian Knot of Generative Models in Healthcare
[article]
2022
arXiv
pre-print
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Furthermore, we summarize the different notions of disentanglement, review approaches to disentangle latent space representations and metrics to evaluate the degree of disentanglement. ...
Encouraging the latent representation of a generative model to be disentangled offers new perspectives of control and interpretability. ...
Multi-Modal Brain Analysis
Results from
Synthetic MRI Modality Generation For Magnetic Resonance (MR) images, different contrast acquisitions show different aspects, important for diagnosis. ...
arXiv:2203.11132v1
fatcat:fxrniu6dtjcz5cumwientkqh7i
Information-based Disentangled Representation Learning for Unsupervised MR Harmonization
[article]
2021
arXiv
pre-print
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CALAMITI learns a disentangled latent space using a unified structure for multi-site harmonization without the need for traveling subjects. ...
In this work, we propose an unsupervised MR harmonization framework, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), based on information bottleneck theory ...
Introduction Magnetic resonance (MR) imaging is a commonly used non-invasive imaging modality due to its flexibility and good tissue contrast. ...
arXiv:2103.13283v1
fatcat:25l2fcymyba2pb4t3euokyvwdu
Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation
[article]
2021
arXiv
pre-print
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Multi-modal medical image segmentation plays an essential role in clinical diagnosis. It remains challenging as the input modalities are often not well-aligned spatially. ...
In this paper, we propose an affinity-guided fully convolutional network for multimodal image segmentation. ...
[12] used disentangled representations to achieve CT and MR adaptation. ...
arXiv:2101.01513v1
fatcat:po63pr66lzcbdlfu3w4fozdpjq
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