Generative Adversarial Networks for Anomaly Detection in Biomedical Imaging: A Study on Seven Medical Image Datasets

Esmaeili, Marzieh; Toosi, Amirhosein; Roshanpoor, Arash; Changizi, Vahid; Ghazisaeedi, Marjan; Sabokrou, Mohammad; Rahmim, Arman

doi:10.1109/access.2023.3244741

Cited by 9 publications

(9 citation statements)

References 76 publications

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“…While GANs have shown promise, they also present challenges related to medical data, such as limited labeled samples and the complex nature of anomalies. In a similar vein, Esmaeili et al [2] investigate the use of GANs for anomaly detection in biomedical imaging. Their study, conducted on seven different medical imaging datasets, shows highly variable performance (AUC: 0.475-0.991; Sensitivity: 0.17-0.98; Specificity: 0.14-0.97), indicating the method's limitations and the need for further research.…”

Section: The Role Of Ai and ML In Anomaly Detectionmentioning

confidence: 99%

Introductory Chapter: Anomaly Detection – Recent Advances, AI and ML Perspectives and Applications

Krishna Parimala

2024

Artificial Intelligence

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Section: The Role Of Ai and ML In Anomaly Detectionmentioning

confidence: 99%

Introductory Chapter: Anomaly Detection – Recent Advances, AI and ML Perspectives and Applications

Krishna Parimala

2024

Artificial Intelligence

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“…With the wide application of Generative Adversarial Networks (GAN) in image generation (Skandarani et al, 2023), these problems are expected to be solved. In recent years GAN has been widely used in medical image tasks such as image segmentation (Beji et al, 2023;Dash et al, 2023;Skandarani et al, 2023;Zhong et al, 2023), lesion classification (Chen et al, 2023;Fan et al, 2023), and lesion detection (Esmaeili et al, 2023;Vyas & Rajendran, 2023). And the study of GAN in medical image synthesis tasks has dominated.…”

Section: Introductionmentioning

confidence: 99%

NC2C-TransCycleGAN: Non-Contrast to Contrast-Enhanced CT Image Synthesis Using Transformer CycleGAN

Hou,

Liu,

Zhang

et al. 2024

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Background: Lung cancer poses a great threat to human life and health. Although the density differences between lesions and normal tissues shown on enhanced CT images is very helpful for doctors to characterize and detect lesions, contrast agents and radiation may cause harm to the health of patients with lung cancer. By learning the mapping relationship between plain CT image and enhanced CT image through deep learning methods, high quality synthetic CECT image results can be generated based on plain scan CT image. It has great potential to help save treatment time and cost of lung cancer patients, reduce radiation dose and expand the medical image dataset in the field of deep learning. Methods: In this study, plain and enhanced CT images of 71 lung cancer patients were retrospectively collected. The data from 58 lung cancer patients were randomly assigned to the training set, and the other 13 cases formed the test set. The Convolution Vison Transformer structure and PixelShuffle operation were combined with CycleGAN, respectively, to help generate clearer images. After random erasing, image scaling and flipping to enhance the training data, paired plain and enhanced CT slices of each patient are input into the network as input and labeled, respectively, for model training.Finally, the peak signal-to-noise ratio, structural similarity and mean square error are used to evaluate the image quality and similarity. Results: The performance of our proposed method is compared with CycleGAN and Pix2Pix on the test set, respectively. The results show that the SSIM value of the enhanced CT images generated by the proposed method improve by 2.00% and 1.39%, the PSNR values improve by 2.05% and 1.71%, and the MSE decreases by 12.50% and 8.53%, respectively, compared with Pix2Pix and CycleGAN. Conclusions: The experimental results show that the improved algorithm based on CylceGAN proposed in this paper can synthesize high-quality lung cancer synthetic enhanced CT images, which is helpful to expand the lung cancer image data set in the deep learning research. More importantly, this method can help lung cancer patients save medical treatment time and cost.

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“…Another benefit is that it should be able to detect any type of anomaly, potentially linked to different diseases. Deep generative models such as variational autoencoders (VAEs) (Kingma and Welling, 2013), generative adversarial networks (GANs) (Goodfellow et al, 2014) and more recently denoising diffusion probabilistic models (DDPMs) (Ho et al, 2020) have shown great results for image generation tasks and unsupervised anomaly detection (UAD) in medical imaging (Esmaeili et al, 2023), including neuroimaging Gong et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET

Hassanaly,

Brianceau,

Solal

et al. 2024

Melba

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Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has gained in popularity. This approach has the great advantage of not requiring tedious pixel-wise data annotation and offers possibility to generalize to any kind of anomalies, including that corresponding to rare diseases. By training a deep generative model with only images from healthy subjects, the model will learn to reconstruct pseudo-healthy images. This pseudo-healthy reconstruction is then compared to the input to detect and localize anomalies. The evaluation of such methods often relies on a ground truth lesion mask that is available for test data, which may not exist depending on the application.<br>We propose an evaluation procedure based on the simulation of realistic abnormal images to validate pseudo-healthy reconstruction methods when no ground truth is available. This allows us to extensively test generative models on different kinds of anomalies and measuring their performance using the pair of normal and abnormal images corresponding to the same subject. It can be used as a preliminary automatic step to validate the capacity of a generative model to reconstruct pseudo-healthy images, before a more advanced validation step that would require clinician’s expertise. We apply this framework to the reconstruction of 3D brain FDG PET using a convolutional variational autoencoder with the aim to detect as early as possible the neurodegeneration markers that are specific to dementia such as Alzheimer’s disease.

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Generative Adversarial Networks for Anomaly Detection in Biomedical Imaging: A Study on Seven Medical Image Datasets

Abstract: This study was part of a PhD dissertation supported by the Tehran University of Medical Sciences (TUMS) with the ethical code of IR.TUMS.SPH.REC.1399.060 from TUMS.

Cited by 9 publications

References 76 publications

Introductory Chapter: Anomaly Detection – Recent Advances, AI and ML Perspectives and Applications

Introductory Chapter: Anomaly Detection – Recent Advances, AI and ML Perspectives and Applications

NC2C-TransCycleGAN: Non-Contrast to Contrast-Enhanced CT Image Synthesis Using Transformer CycleGAN

Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET

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