Creating Artificial Images for Radiology Applications Using Generative Adversarial Networks (GANs) – A Systematic Review

Sorin, Vera; Barash, Yiftach; Konen, Eli; Klang, Eyal

doi:10.1016/j.acra.2019.12.024

Cited by 124 publications

(94 citation statements)

References 41 publications

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“…In recent years, deep learning (DL) has been introduced into computer-aided diagnosis (CAD) systems to improve the accuracy of medical imaging diagnosis, save time, and explore new directions and opportunities in radiology. 6,7 Applying CAD systems may not only reduce radiologists' workload but also lessen subjective and ambiguous reporting. Many CAD studies on thyroid imaging have been performed, [8][9][10][11][12] including the application of CAD to ultrasound images for the discrimination of benign and malignant thyroid nodules 8,9 and CT-based CAD 10,11 for the detection of thyroid abnormalities.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for intelligent diagnosis in thyroid scintigraphy

et al. 2021

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Objective To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy. Methods We constructed DL models with AlexNet, VGGNet, and ResNet. The models were trained separately with transfer learning. We measured each model’s performance with six indicators: recall, precision, negative predictive value (NPV), specificity, accuracy, and F1-score. We also compared the diagnostic performances of first- and third-year nuclear medicine (NM) residents with assistance from the best-performing DL-based model. The Kappa coefficient and average classification time of each model were compared with those of two NM residents. Results The recall, precision, NPV, specificity, accuracy, and F1-score of the three models ranged from 73.33% to 97.00%. The Kappa coefficient of all three models was >0.710. All models performed better than the first-year NM resident but not as well as the third-year NM resident in terms of diagnostic ability. However, the ResNet model provided “diagnostic assistance” to the NM residents. The models provided results at speeds 400 to 600 times faster than the NM residents. Conclusion DL-based models perform well in diagnostic assessment by thyroid scintigraphy. These models may serve as tools for NM residents in the diagnosis of Graves’ disease and subacute thyroiditis.

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Section: Introductionmentioning

confidence: 99%

Deep learning for intelligent diagnosis in thyroid scintigraphy

et al. 2021

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“…GANs have been used to synthesize strikingly realistic pictures of faces and inanimate objects (11,12). With respect to medical image data, GANs have shown promise across a wide range of applications (13), including simulated modality transformations (14)(15)(16)(17)(18), artifact reduction (19,20), and synthetic-image generation for supervised machine learning, thereby obviating patient-privacy protection of training data (21)(22)(23).…”

Section: Generative Adversarial Network (Gans)mentioning

confidence: 99%

Artificial intelligence in molecular imaging

Herskovits¹

2021

Ann Transl Med

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AI has, to varying degrees, affected all aspects of molecular imaging, from image acquisition to diagnosis. During the last decade, the advent of deep learning in particular has transformed medical image analysis. Although the majority of recent advances have resulted from neural-network models applied to image segmentation, a broad range of techniques has shown promise for image reconstruction, image synthesis, differential-diagnosis generation, and treatment guidance. Applications of AI for drug design indicate the way forward for using AI to facilitate molecular-probe design, which is still in its early stages.Deep-learning models have demonstrated increased efficiency and image quality for PET reconstruction from sinogram data. Generative adversarial networks (GANs), which are paired neural networks that are jointly trained to generate and classify images, have found applications in modality transformation, artifact reduction, and synthetic-PET-image generation. Some AI applications, based either partly or completely on neural-network approaches, have demonstrated superior differential-diagnosis generation relative to radiologists. However, AI models have a history of brittleness, and physicians and patients may not trust AI applications that cannot explain their reasoning. To date, the majority of molecular-imaging applications of AI have been confined to research projects, and are only beginning to find their ways into routine clinical workflows via commercialization and, in some cases, integration into scanner hardware. Evaluation of actual clinical products will yield more realistic assessments of AI's utility in molecular imaging.

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“…Hence the novel data generated by GANs can be used to improve research, education, and patient management. 9,42…”

Section: Current Trends and Techniques Used For Ai In Imagingmentioning

confidence: 99%

An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging

Goyal¹

2021

RMI

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This review article aims to discuss current trends, techniques, and promising uses of artificial intelligence (AI) in breast imaging, apart from the pitfalls that may hinder its progress. It includes only the commonly used and basic terminology imperative for physicians to know. AI is not just a computerized approach but an interface between humans and machines. Apart from reducing workload and improved diagnostic accuracy, radiologists get more time for patient care or clinical work by using various machine learning techniques that augment their productivity. Inadequate data input with suboptimal pattern recognition, data extraction challenges, legal implications, and exorbitant costs are a few pitfalls that AI algorithms still face while analyzing and giving appropriate outcomes. Various machine learning approaches are used to construct prediction models for clinical decision support and ameliorating patient management. Since AI is still in its fledgling state, with many limitations for clinical implementation, clinical support and feedback are needed to avoid algorithmic errors. Hence, both machine learning and human insight complement each other in revolutionizing breast imaging.

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Creating Artificial Images for Radiology Applications Using Generative Adversarial Networks (GANs) – A Systematic Review

Cited by 124 publications

References 41 publications

Deep learning for intelligent diagnosis in thyroid scintigraphy

Deep learning for intelligent diagnosis in thyroid scintigraphy

Artificial intelligence in molecular imaging

An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging

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