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Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement [article]

Uddeshya Upadhyay, Viswanath P. Sudarshan, Suyash P. Awate
2021 arXiv   pre-print
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]

Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir
2021 arXiv   pre-print
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]

Viswanath P. Sudarshan, Uddeshya Upadhyay, Gary F. Egan, Zhaolin Chen, Suyash P. Awate
2021 arXiv   pre-print
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

Bennett A. Landman, Ivana Išgum
2020 Medical Imaging 2020: Image Processing  
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]

Uddeshya Upadhyay, Yanbei Chen, Zeynep Akata
2021 arXiv   pre-print
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]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
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  
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

Ivana Išgum, Olivier Colliot
2022 Medical Imaging 2022: Image Processing  
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

Xiang Chen, Andres Diaz-Pinto, Nishant Ravikumar, Alejandro Frangi
2020 Progress in Biomedical Engineering  
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]

Jue Jiang, Yu Chi Hu, Neelam Tyagi, Andreas Rimner, Nancy Lee, Joseph O. Deasy, Sean Berry, Harini Veeraraghavan
2020 arXiv   pre-print
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]

Xuxin Chen, Ximin Wang, Ke Zhang, Roy Zhang, Kar-Ming Fung, Theresa C. Thai, Kathleen Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu
2021 arXiv   pre-print
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

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
2020 Frontiers in Cardiovascular Medicine  
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

Anneke Meyer, Alireza Mehrtash, Marko Rak, Oleksii Bashkanov, Bjoern Langbein, Alireza Ziaei, Adam S. Kibel, Clare M. Tempany, Christian Hansen, Junichi Tokuda
2021 Scientific Reports  
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]

Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, Xiaowei Ding
2020 arXiv   pre-print
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]

Reza Azad, Nika Khosravi, Mohammad Dehghanmanshadi, Julien Cohen-Adad, Dorit Merhof
2022 arXiv   pre-print
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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