Perceived Realism of High-Resolution Generative Adversarial                     Network–derived Synthetic Mammograms

Korkinof, Dimitrios; Harvey, Hugh; Heindl, Andreas; Karpati, Edith; Williams, G.; Rijken, Tobias; Kecskemethy, Peter D.; Glocker, Ben

doi:10.1148/ryai.2020190181

Cited by 15 publications

(12 citation statements)

References 8 publications

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“…Only one reader was able to reliably distinguish between CNN T2 and real T2 maps, and even then, this reader did not grade one image higher quality than the other. These findings suggest that the cGAN was able to accurately reproduce the visual features of the original T2 map, despite only seeing standard anatomic MRI sequences, and are consistent with a recent work showing visual similarities between GAN-generated and real mammograms as determined by radiologists (9). One additional interesting finding was that the CNN T2 maps sometimes removed artifacts present in the real T2 maps, suggesting that these synthetic images might provide an improvement over the original images in some cases.…”

supporting

confidence: 89%

Radiology Alchemy: GAN We Do It?

Fritz

2021

Radiology: Artificial Intelligence

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supporting

confidence: 89%

Radiology Alchemy: GAN We Do It?

Fritz

2021

Radiology: Artificial Intelligence

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“…Within the EU, GDPR continues to pose challenges to researchers looking to maximise their quantity of data. Two clear potential avenues that could be explored further to alleviate this are the use of deep learning to create synthetic data for augmentation [ 28 ] and the process of obtaining prospective informed consent from patients for the use of their medical imaging data [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE)

et al. 2022

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Objective There has been a large amount of research in the field of artificial intelligence (AI) as applied to clinical radiology. However, these studies vary in design and quality and systematic reviews of the entire field are lacking.This systematic review aimed to identify all papers that used deep learning in radiology to survey the literature and to evaluate their methods. We aimed to identify the key questions being addressed in the literature and to identify the most effective methods employed. Methods We followed the PRISMA guidelines and performed a systematic review of studies of AI in radiology published from 2015 to 2019. Our published protocol was prospectively registered. Results Our search yielded 11,083 results. Seven hundred sixty-seven full texts were reviewed, and 535 articles were included. Ninety-eight percent were retrospective cohort studies. The median number of patients included was 460. Most studies involved MRI (37%). Neuroradiology was the most common subspecialty. Eighty-eight percent used supervised learning. The majority of studies undertook a segmentation task (39%). Performance comparison was with a state-of-the-art model in 37%. The most used established architecture was UNet (14%). The median performance for the most utilised evaluation metrics was Dice of 0.89 (range .49–.99), AUC of 0.903 (range 1.00–0.61) and Accuracy of 89.4 (range 70.2–100). Of the 77 studies that externally validated their results and allowed for direct comparison, performance on average decreased by 6% at external validation (range increase of 4% to decrease 44%). Conclusion This systematic review has surveyed the major advances in AI as applied to clinical radiology. Key Points • While there are many papers reporting expert-level results by using deep learning in radiology, most apply only a narrow range of techniques to a narrow selection of use cases. • The literature is dominated by retrospective cohort studies with limited external validation with high potential for bias. • The recent advent of AI extensions to systematic reporting guidelines and prospective trial registration along with a focus on external validation and explanations show potential for translation of the hype surrounding AI from code to clinic.

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“…Existing GAN studies on breast imaging (e.g., mammography and MRI) have been reported [36] , [37] . Modanwal et al proposed a normalization method for breast MRI using a cycle-consistent GAN [38] .…”

Section: Discussionmentioning

confidence: 99%

Tumor-Attentive Segmentation-Guided GAN for Synthesizing Breast Contrast-Enhanced MRI Without Contrast Agents

Kim

Cho

Kwon

et al. 2023

IEEE J. Transl. Eng. Health Med.

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Objective: Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive imaging technique critical for breast cancer diagnosis. However, the administration of contrast agents poses a potential risk. This can be avoided if contrast-enhanced MRI can be obtained without using contrast agents. Thus, we aimed to generate T1-weighted contrast-enhanced MRI (ceT1) images from pre-contrast T1 weighted MRI (preT1) images in the breast. Methods: We proposed a generative adversarial network to synthesize ceT1 from preT1 breast images that adopted a local discriminator and segmentation task network to focus specifically on the tumor region in addition to the whole breast. The segmentation network performed a related task of segmentation of the tumor region, which allowed important tumor-related information to be enhanced. In addition, edge maps were included to provide explicit shape and structural information. Our approach was evaluated and compared with other methods in the local (n = 306) and external validation (n = 140) cohorts. Four evaluation metrics of normalized mean squared error (NRMSE), Pearson cross-correlation coefficients (CC), peak signal-to-noise ratio (PSNR), and structural similarity index map (SSIM) for the whole breast and tumor region were measured. An ablation study was performed to evaluate the incremental benefits of various components in our approach. Results: Our approach performed the best with an NRMSE 25.65, PSNR 54.80 dB, SSIM 0.91, and CC 0.88 on average, in the local test set. Conclusion: Performance gains were replicated in the validation cohort. Significance: We hope that our method will help patients avoid potentially harmful contrast agents. Clinical and Translational Impact Statement—Contrast agents are necessary to obtain DCE-MRI which is essential in breast cancer diagnosis. However, administration of contrast agents may cause side effects such as nephrogenic systemic fibrosis and risk of toxic residue deposits. Our approach can generate DCE-MRI without contrast agents using a generative deep neural network. Thus, our approach could help patients avoid potentially harmful contrast agents resulting in an improved diagnosis and treatment workflow for breast cancer.

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Perceived Realism of High-Resolution Generative Adversarial Network–derived Synthetic Mammograms

Cited by 15 publications

References 8 publications

Radiology Alchemy: GAN We Do It?

Radiology Alchemy: GAN We Do It?

Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE)

Tumor-Attentive Segmentation-Guided GAN for Synthesizing Breast Contrast-Enhanced MRI Without Contrast Agents

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