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Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement
[article]
2021
arXiv
pre-print
Preserved Fulltext
This paper proposes a GAN-based framework that (i)~models an adaptive loss function for robustness to OOD-noisy data that automatically tunes the spatially varying norm for penalizing the residuals and ...
This demands robust methods that can quantify uncertainty in the prediction for making informed decisions, especially for critical areas such as medical imaging. ...
In this work, we proposed a GAN-based framework with adaptive quasi-norm loss functions for improved robustness to unseen perturbations on test data. ...
arXiv:2110.03343v1
fatcat:3szhroycjfhijmnz2w5nfo7wny
A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions
[article]
2021
arXiv
pre-print
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The recent advancements in Generative Adversarial Networks (GANs) in computer vision as well as in medical imaging may provide a basis for enhanced capabilities in cancer detection and analysis. ...
With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on GANs in the artificial intelligence community. ...
Chest and lungs
Jiang et al (2018 [126]
CycleGAN-based
NSCLC [318]
CT, MRI
Cross-domain
translation
Dice: n.a.
0.70
Tumour-aware loss for unsupervised cross-domain adaptation. ...
arXiv:2107.09543v1
fatcat:jz76zqklpvh67gmwnsdqzgq5he
Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Data
[article]
2021
arXiv
pre-print
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multi-contrast MRI images, leading to improved robustness of suDNN to OOD acquisitions. ...
Recent deep-neural-network (DNN) based methods for image-to-image translation enable the mapping of low-quality PET images (acquired using substantially reduced dose), coupled with the associated magnetic ...
Acknowledgment The authors are grateful for support from the Infrastructure Facility for Ad- ...
arXiv:2107.09892v1
fatcat:cvixpr6hvbg5tojcruwettjiae
Front Matter: Volume 11313
2020
Medical Imaging 2020: Image Processing
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These two-number sets start with 00, 01, 02, 03, 04, ...
The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon. ...
loss for GAN-based super-resolution of clinical CT images using micro CT image database 11313 07 GANet: group attention network for diabetic retinopathy image segmentation 11313 08 Fully automated segmentation ...
doi:10.1117/12.2570657
fatcat:be32besqknaybh6wibz7unuboa
Robustness via Uncertainty-aware Cycle Consistency
[article]
2021
arXiv
pre-print
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To address this, we propose a novel probabilistic method based on Uncertainty-aware Generalized Adaptive Cycle Consistency (UGAC), which models the per-pixel residual by generalized Gaussian distribution ...
, maps, facades, and also in medical imaging domain consisting of MRI. ...
The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Uddeshya Upadhyay. ...
arXiv:2110.12467v1
fatcat:owggkflpu5c25mxqy4qxhlqwyu
A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
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491 An Open Framework Enabling Electromagnetic Tracking in Image-Guided Interventions 492 Small Lesion Classication in Dynamic Contrast Enhancement MRI for Breast Cancer Early Detection 494 Uncertainty ...
Transduction for Identifying Disease Comorbidity Patterns 659 Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound 660 Tumor-aware, Adversarial Domain Adaptation ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
2021 Index IEEE Transactions on Image Processing Vol. 30
2021
IEEE Transactions on Image Processing
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, TIP 2021 7317-7332 Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild. ...
., +, TIP 2021 7446-7457 Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild. ...
doi:10.1109/tip.2022.3142569
fatcat:z26yhwuecbgrnb2czhwjlf73qu
Front Matter: Volume 12032
2022
Medical Imaging 2022: Image Processing
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These two-number sets start with 00, ...
atlas-based features in graph convolutional nets [12032-45] REGISTRATION 18 MedRegNet: unsupervised multimodal retinal-image registration with GANs and ranking loss [12032-35] 19 Motion correction in retinal ...
for deep learning segmentation in medical imaging [12032-25] 10 Unsupervised domain adaptation for segmentation with black-box source model [12032-26] 11 Do I know this? ...
doi:10.1117/12.2638192
fatcat:ikfgnjefaba2tpiamxoftyi6sa
Deep learning in medical image registration
2020
Progress in Biomedical Engineering
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With the advent of deep learning, there have been significant advances in algorithmic performance for various computer vision tasks in recent years, including medical image registration. ...
of unmet clinical needs and potential directions for future research in deep learning-based medical image registration. ...
Instead of the TV loss, Stergios et al [53] proposed a network similar to 'CNN+STN' with L1 regularisation for 3D lung MRI image registration. ...
doi:10.1088/2516-1091/abd37c
fatcat:74w7ra4f7nfrrpfk2ifvmijntq
PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation
[article]
2020
arXiv
pre-print
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resonance (MRI) images. ...
The structure discriminator computes structure of interest focused adversarial loss by combining the generated pseudo MRI with probabilistic segmentations produced by a simultaneously trained segmentation ...
MRI is the target domain that is provided with only MRI images x m ∈ X M for training. ...
arXiv:2007.09465v1
fatcat:mzm7lgdjjzc63jaesk2jwmesqq
Recent advances and clinical applications of deep learning in medical image analysis
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2105.13381v1 fatcat:yxa6asxvcra3bixp4habjdbcbaOther Versions
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging ...
scenarios, including classification, segmentation, detection, and image registration. ...
The uncertainty-aware teacher model can produce more reliable guidance for the student model, and the student model could in turn improve the teacher model. ...
arXiv:2105.13381v2
fatcat:2k342a6rhjaavpoa2qoqxhg5rq
Deep Learning for Cardiac Image Segmentation: A Review
2020
Frontiers in Cardiovascular Medicine
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In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography ...
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. ...
As a result, a segmentation network designed for non-contrast enhanced images may not be directly applied to contrast-enhanced images (100) . ...
doi:10.3389/fcvm.2020.00025
pmid:32195270
pmcid:PMC7066212
fatcat:iw7xpnltn5cgbn5ullq2ldy3nq
Domain adaptation for segmentation of critical structures for prostate cancer therapy
2021
Scientific Reports
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As network models trained on data from a single source suffer from quality loss due to the domain shift, we propose a semi-supervised domain adaptation (DA) method to refine the model's performance in ...
Our DA method combines transfer learning and uncertainty guided self-learning based on deep ensembles. ...
The Titan Xp used for this research was donated by the NVIDIA Corporation. ...
doi:10.1038/s41598-021-90294-4
pmid:34075061
fatcat:3rvdni6btngnbk4u4jnpme2m24
Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation
[article]
2020
arXiv
pre-print
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data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations. ...
We hope this survey article increases the community awareness of the techniques that are available to handle imperfect medical image segmentation datasets. ...
A recurring theme in many of the domain adaptation papers discussed in this section is the use of GANs, CycleGANs, or some sort of adversarial loss for the purpose of image reconstruction. ...
arXiv:1908.10454v2
fatcat:mjvfbhx75bdkbheysq3r7wmhdi
Medical Image Segmentation on MRI Images with Missing Modalities: A Review
[article]
2022
arXiv
pre-print
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Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming their negative repercussions is considered a hurdle in biomedical imaging. ...
The main goal of this research is to offer a performance evaluation of missing modality compensating networks, as well as to outline future strategies for dealing with this issue. ...
According to [16] annotating the object of interest (e.g. brain tumour) in MRI images always come with uncertainty and mistakes, thus, both humans and algorithms may contribute to the deteriorating of ...
arXiv:2203.06217v1
fatcat:wbfhesrpajdy3pfqbnut6il32e
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