Synthetic medical image generator for data augmentation and anonymisation based on generative adversarial network for glioblastoma tumors growth prediction

Kamli, Adel; Saouli, Rachida; Batatia, Hadj; Naceur, Mostefa Ben; Youkana, Imane

doi:10.1049/iet-ipr.2020.1141

Cited by 7 publications

(9 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…10 , incorporate an indirect evaluation of the generated images. Additionally, seventeen [ 38 , 46 – 49 , 54 – 57 , 60 , 67 , 69 , 75 , 78 , 79 , 81 , 92 ] of these studies visualize a selection of the synthetic samples, and thirteen [ 39 , 41 , 42 , 44 , 58 , 66 , 68 , 82 , 83 , 85 , 89 – 91 ] were qualitatively evaluated by experienced clinicians, as illustrated in Fig. 11 .…”

Section: Discussionmentioning

confidence: 99%

“…There are different strategies to mitigate the limited population of the original dataset, including subsampling of examinations at a slice level (from 3D volume to 2D slices, twenty-six studies) [ 38 , 39 , 41 , 42 , 44 , 47 – 50 , 54 , 55 , 57 , 58 , 60 , 66 – 69 , 75 , 77 , 79 , 81 – 84 , 88 ] and at a patch level (from tumor to sub-regions of the tumor, seven studies) [ 56 , 72 , 78 , 85 , 89 – 91 ]. These subsampling techniques can result in the loss of key features of tumor heterogeneity, significant voxel-based and spatial information with morphological features such as sphericity, shape and volume.…”

Section: Discussionmentioning

confidence: 99%

“…Random noise vectors have been utilized as input for data generation in twenty-one [ 38 , 39 , 41 , 42 , 44 , 49 , 52 , 54 , 72 , 77 – 79 , 81 – 85 , 88 – 91 ] and pixel-level lesion annotations in twelve studies [ 46 – 48 , 50 , 58 , 60 , 66 – 69 , 75 , 92 ]. Although this ensures the generation of different types of tissue, including tumor regions, it may lead to less variety in terms of shape and volume of the examined anatomical regions.…”

Section: Discussionmentioning

confidence: 99%

“…Regional legislative frameworks for privacy are posing significant challenges in medical data analysis. Deep generative models can assist in providing full anonymity of medical data [ 44 , 46 – 48 , 72 , 75 , 88 ], even on an image level, making it easier to share them specifically the synthetic version of them.…”

Section: Discussionmentioning

confidence: 99%

“…Kamli et al [ 75 ] incorporated in the proposed adversarial pipeline an anonymizing model [ 46 ], to increase prediction performance in patients with glioblastoma multiforme tumor growth. The authors did not modify the tumor size and shape as in [ 46 ], because any modifications on these parameters could affect the accurate prediction of tumor growth.…”

Section: Deep Generative Models By Anatomical Regionmentioning

confidence: 99%

See 4 more Smart Citations

Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review

et al. 2022

View full text Add to dashboard Cite

Contemporary deep learning-based decision systems are well-known for requiring high-volume datasets in order to produce generalized, reliable, and high-performing models. However, the collection of such datasets is challenging, requiring time-consuming processes involving also expert clinicians with limited time. In addition, data collection often raises ethical and legal issues and depends on costly and invasive procedures. Deep generative models such as generative adversarial networks and variational autoencoders can capture the underlying distribution of the examined data, allowing them to create new and unique instances of samples. This study aims to shed light on generative data augmentation techniques and corresponding best practices. Through in-depth investigation, we underline the limitations and potential methodology pitfalls from critical standpoint and aim to promote open science research by identifying publicly available open-source repositories and datasets.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Deep Generative Models By Anatomical Regionmentioning

confidence: 99%

See 3 more Smart Citations

Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review

et al. 2022

View full text Add to dashboard Cite

show abstract

A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

Mohammed

2023

WIREs Computational Stats

View full text Add to dashboard Cite

In machine learning, a generative model is responsible for generating new samples of data in terms of a probabilistic model. Generative adversarial network (GAN) has been widely used to generate realistic samples in different domains and outperforms its peers in the generative models family. However, producing a robust GAN model is not a trivial task because many challenges face the GAN during the training process and impact its performance, affecting the quality and diversity of the generated samples. In this article, we conduct a comprehensive review of GANs to present the fundamentals of GAN, including its components, types, and objective functions. Also, we present an overview of the evaluation matrices used to evaluate GAN models. Moreover, we list the applications of GANs and research work in various domains. Finally, we present the challenges that face GANs and highlight two significant issues, representing mode collapse and training instability, in addition to those research efforts that tackle these challenges.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Deep Learning Statistical Learning and Exploratory Methods of the Data Sciences > Neural Networks

show abstract

The use of generative adversarial networks in medical image augmentation

Makhlouf,

Maayah,

Abughanam

et al. 2023

Neural Comput & Applic

View full text Add to dashboard Cite

Generative Adversarial Networks (GANs) have been widely applied in various domains, including medical image analysis. GANs have been utilized in classification and segmentation tasks, aiding in the detection and diagnosis of diseases and disorders. However, medical image datasets often suffer from insufficiency and imbalanced class distributions. To overcome these limitations, researchers have employed GANs to generate augmented medical images, effectively expanding datasets and balancing class distributions. This review follows the PRISMA guidelines and systematically collects peer-reviewed articles on the development of GAN-based augmentation models. Automated searches were conducted on electronic databases such as IEEE, Scopus, Science Direct, and PubMed, along with forward and backward snowballing. Out of numerous articles, 52 relevant ones published between 2018 and February 2022 were identified. The gathered information was synthesized to determine common GAN architectures, medical image modalities, body organs of interest, augmentation tasks, and evaluation metrics employed to assess model performance. Results indicated that cGAN and DCGAN were the most popular GAN architectures in the reviewed studies. Medical image modalities such as MRI, CT, X-ray, and ultrasound, along with body organs like the brain, chest, breast, and lung, were frequently used. Furthermore, the developed models were evaluated, and potential challenges and future directions for GAN-based medical image augmentation were discussed. This review presents a comprehensive overview of the current state-of-the-art in GAN-based medical image augmentation and emphasizes the potential advantages and challenges associated with GAN utilization in this domain.

show abstract

Synthetic medical image generator for data augmentation and anonymisation based on generative adversarial network for glioblastoma tumors growth prediction

Cited by 7 publications

References 37 publications

Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review

Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review

A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

The use of generative adversarial networks in medical image augmentation

Contact Info

Product

Resources

About