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Generative Adversarial Network in Medical Imaging: A Review [article]

Xin Yi, Ekta Walia, Paul Babyn
2019 arXiv   pre-print
, 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

Manal AlAmir, Manal AlGhamdi
2022 ACM Computing Surveys  
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]

Jana Fragemann, Lynton Ardizzone, Jan Egger, Jens Kleesiek
2022 arXiv   pre-print
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

Sarfaraz Hussein, Pujan Kandel, Candice W. Bolan, Michael B. Wallace, Ulas Bagci
2019 IEEE Transactions on Medical Imaging  
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]

Jelmer M. Wolterink and Konstantinos Kamnitsas and Christian Ledig and Ivana Išgum
2018 arXiv   pre-print
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

Giorgio Ciano, Paolo Andreini, Tommaso Mazzierli, Monica Bianchini, Franco Scarselli
2021 Mathematics  
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

Vignesh Sampath, Iñaki Maurtua, Juan José Aguilar Martín, Aitor Gutierrez
2021 Journal of Big Data  
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

Md Mamunur Rahaman, Chen Li, Xiangchen Wu, Yudong Yao, Zhijie Hu, Tao Jiang, Xiaoyan Li, Shouliang Qi
2020 IEEE Access  
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

Atsushi TERAMOTO
Medical Imaging and Information Sciences  
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]

Agisilaos Chartsias, University Of Edinburgh, Sotirios Tsaftaris, Javier Escudero Rodriguez
2021
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

Isabella Poles
2022
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]

Δημήτριος Ε. Διαμαντής, University Of Thessaly, Δημήτριος Ιακωβίδης
2021
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

Sergei Issaev
2021
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