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Generative Adversarial Network in Medical Imaging: A Review
[article]
2019
arXiv
pre-print
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, classification, and cross-modality synthesis. ...
This has proven to be useful in many cases, such as domain adaptation, data augmentation, and image-to-image translation. ...
It has been adopted in MedGAN (Armanious et al., 2018) for evaluation of the generated image quality but it would be interesting to see its effectiveness for different types of medical images as compared ...
arXiv:1809.07294v3
fatcat:5j5i6shlcvbbjm74ceidzg6rc4
The Role of Generative Adversarial Network in Medical Image Analysis: An in-depth survey
2022
ACM Computing Surveys
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Fourth, the applications of GAN in medical images including cross-modality, augmentation, detection, classification, and reconstruction were illustrated. ...
A generative adversarial network (GAN) is one of the most significant research directions in the field of artificial intelligence, and its superior data generation capability has garnered wide attention ...
Each type of medical image has its own purposes, strengths, and limitations. ...
doi:10.1145/3527849
fatcat:m5yjmhlxjrfoblw6cxwaqbb774
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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Deep neural networks are commonly used for medical purposes such as image generation, segmentation, or classification. ...
After introducing the theoretical frameworks, we give an overview of recent medical applications and discuss the impact and importance of disentanglement approaches for medical applications. ...
Synthetic CT Image Generation The authors of [Toda et al., 2021] use a modified version of an InfoGAN [Chen et al., 2016] and a Wasser-steinGAN [Arjovsky et al., 2017] to generate synthetic images ...
arXiv:2203.11132v1
fatcat:fxrniu6dtjcz5cumwientkqh7i
Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches
2019
IEEE Transactions on Medical Imaging
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2018 pre-print arXiv:1801.03230v2 fatcat:vod6h4d3bjh4xlq2vd43qkwnjuOther Versions
We evaluate our proposed supervised and unsupervised learning algorithms on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans, respectively, and obtain the state-of-the-art ...
We also seek the answer to the fundamental question about the goodness of "deep features" for unsupervised tumor classification. ...
Moreover, the applications of CatGAN [45] and InfoGAN [46] for semisupervised and unsupervised classification tasks in medical imaging are worth exploring as well. ...
doi:10.1109/tmi.2019.2894349
pmid:30676950
fatcat:woorhrucqjbcxhulknvh2irjta
Generative adversarial networks and adversarial methods in biomedical image analysis
[article]
2018
arXiv
pre-print
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Adversarial techniques have been extensively used to synthesize and analyze biomedical images. ...
We conclude with a discussion of strengths and limitations of adversarial methods in biomedical image analysis, and propose potential future research directions. ...
This type of adversarial training has found its way to many applications in biomedical image analysis. ...
arXiv:1810.10352v1
fatcat:ixx7aizkujckvgov5f6nnrbk5e
A Multi-Stage GAN for Multi-Organ Chest X-ray Image Generation and Segmentation
2021
Mathematics
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In this paper, we present a novel multi-stage generation algorithm based on Generative Adversarial Networks (GANs) that can produce synthetic images along with their semantic labels and can be used for ...
The main feature of the method is that, unlike other approaches, generation occurs in several stages, which simplifies the procedure and allows it to be used on very small datasets. ...
It is worth observing that our method can be employed for any type of image, not exclusively medical ones, while synthetic and real images can concur in solving the segmentation problem (being used for ...
doi:10.3390/math9222896
fatcat:zes5er7wznfxbfhkgqpvx4irsy
A survey on generative adversarial networks for imbalance problems in computer vision tasks
2021
Journal of Big Data
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It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets ...
The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. ...
Also, we acknowledge the members of the Autonomous and Intelligent Systems Unit, Tekniker, for valuable discussions and collaborations. ...
doi:10.1186/s40537-021-00414-0
pmid:33552840
pmcid:PMC7845583
fatcat:g3p6hbjuj5c5vbe23ms4g6ed6q
A Survey for Cervical Cytopathology Image Analysis Using Deep Learning
2020
IEEE Access
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Then, a thorough review of the recent development of deep learning for the segmentation and classification of cervical cytology images is presented. ...
Cervical cancer is one of the most common and deadliest cancers among women. Despite that, this cancer is entirely treatable if it is detected at a precancerous stage. ...
The authors first thank Zixian Li and Guoxian Li for their important discussion and also Afnan Ghazi and B. E. Frank Kulwa for their great proofreading work. ...
doi:10.1109/access.2020.2983186
fatcat:pagpfdi2brgoxfywds455vtfhy
Generation of Lung Nodule Using Generative Adversarial Networks and Its Application for AI-CAD
敵対的生成ネットワークによる肺結節CT画像の生成とAI-CADへの応用
Medical Imaging and Information Sciences
敵対的生成ネットワークによる肺結節CT画像の生成とAI-CADへの応用
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Many AI-CADs have been developed to assist physicians in the diagnosis of lung cancer using CT images. ...
In this review article, we describe the generation of lung nodule images by generative adversarial networks and its application to AI-CADs as one of the solutions to this challenge. ...
Tsujimoto, et al. : Synthetic CT Image Generation of Shape-Controlled Lung Cancer using Semi-Conditional InfoGAN and Its Applicability for Type Classification," IJCARS, 16, 241-251, 2021. ...
doi:10.11318/mii.38.57
fatcat:md53u33sn5cx3f7mguhrynbz7a
Multimodal and disentangled representation learning for medical image analysis
[article]
2021
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MR) to images in another (e.g. CT). This is useful especially in cardiac datasets, where different spatial and temporal resolutions make image pairing difficult, if not impossible. ...
Common and unique information is combined into a fused representation, that is robust to missing modalities, and can be decoded into synthetic images of the target modalities. ...
Different types of disentanglement, for example that of lung nodule from the background has been proposed for lung nodule synthesis [142] . ...
doi:10.7488/era/767
fatcat:25dlmeyl2rfdnaugsk3hdox7gy
AbdoMReg: A Deep Learning Framework for Abdominal MR Deformable Image Registration
2022
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AbdoMReg proposes data engineering tools to provide normalized and synthetic data generation, monomodal and multimodal intrapatient and monomodal interpatient image registrations, novel loss function terms ...
Nowadays, novel imaging techniques benefit various clinical applications ranging from patient diagnosis and follow-up to real-time medical imaging integration to guide critical surgical procedures. ...
Multiple types of GANs are conditional GAN (cGan) [87] , InfoGan [88] , CycleGAN [89] , and StarGan [90] . ...
doi:10.25417/uic.20253942
fatcat:kkm4vrzy7fefzkogmtgbtkyaqy
Intelligent systems and services for image and video analysis
[article]
2021
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To battle this problem, in the context of inflammatory conditions detection in WCE images, a novel approach is presented that uses Generative Adversarial Networks (GANs) to generate artificial images. ...
In view of scientific challenges for developing innovative solutions with a broad social impact, it investigates applications in biomedicine and computer-assisted navigation of visually impaired individuals ...
GANs have also been used for synthetic segmentation images of the lungs and heart in chest X-ray scans (Dai et al. 2017) . The work of (C. ...
doi:10.26253/heal.uth.13375
fatcat:4fay4wumojfndc5b6cokz2wijq
Robust image-to-image translation tool for fibrotic quantification of whole-slide images
2021
Preserved Fulltext
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A dataset consisting of 32,652 PSR-stained source images paired with their manually translated counterpart was used to train a conditional conditional generative adverserial network. ...
The source images consist of murine diaphragm, liver, and tibialis anterior sections, varying in lighting and staining conditions. ...
), creating segmentations (such as segmenting nuclei), or even performing tissue classification (for example cancerous or non-cancerous). ...
doi:10.14288/1.0402505
fatcat:l6ppyietbbd47gehohw7ue7j6i