RDA-UNET-WGAN: An Accurate Breast Ultrasound Lesion Segmentation Using Wasserstein Generative Adversarial Networks

Negi, Anuja; Raj, Alex Noel Joseph; Nersisson, Ruban; Zhuang, Zhemin; Murugappan, M.

doi:10.1007/s13369-020-04480-z

Cited by 62 publications

(23 citation statements)

References 11 publications

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“…The advancing of ultrasound technology allows for a wide application of Color Doppler in numerous diseases; this approach has a high sensitivity to blood flow and can reflect the the hemodynamics of the lesions, with a promising clinical application prospect. It is generally believed in the academic circles that there are differences in vascular morphology among masses of different natures: malignant masses are usually rich in blood vessels, but most of them are neovascularized and immature, while benign masses have mature and complete blood vessels, laying the foundation to distinguish benign and malignant masses with a good clinical application prospect [21][22][23][24][25]. In this study, the percentage of Adler grade II-III of the benign masses was lower than that of the malignant masses (53.33% vs. 86.67%), indicating that CDFI can be a scientific basis to determine the nature of the masses.…”

Section: Discussionmentioning

confidence: 99%

Combination of shear wave elastography and BI-RADS in identification of solid breast masses

Zheng

Xuan

et al. 2021

BMC Med Imaging

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Background To explore the value of quantitative shear wave elastography (SWE) plus the Breast Imaging Reporting and Data System (BI-RADS) in the identification of solid breast masses. Methods A total of 108 patients with 120 solid breast masses admitted to our hospital from January 2019 to January 2020 were enrolled in this study. The pathological examination served as the gold standard for definitive diagnosis. Both SWE and BI-RADS grading were performed. Results Out of the 120 solid breast masses in 108 patients, 75 benign and 45 malignant masses were pathologically confirmed. The size, shape, margin, internal echo, microcalcification, lateral acoustic shadow, and posterior acoustic enhancement of benign and malignant masses were significantly different (all P < 0.05). The E mean, E max, SD, and E ratio of benign and malignant masses were significantly different (all P < 0.05). The E min was similar between benign and malignant masses (P > 0.05). The percentage of Adler grade II-III of the benign masses was lower than that of the malignant masses (P < 0.05). BI-RADS plus SWE yielded higher diagnostic specificity and positive predictive value than either BI-RADS or SWE; BI-RADS plus SWE yielded the highest diagnostic accuracy among the three methods (all P < 0.05). Conclusion SWE plus routine ultrasonography BI-RADS has a higher value in differentiating benign from malignant breast masses than color doppler or SWE alone, which should be further promoted in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Combination of shear wave elastography and BI-RADS in identification of solid breast masses

Zheng

Xuan

et al. 2021

BMC Med Imaging

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“…p data and p z are the real and generated data distributions, respectively. D(x) gives the discriminator output value of the real sample, x, probability [35]. G(z) is the noisy sample generated by G. The function of D is to correctly identify its input data source, such that the resulted output of D(x) = 1 if the data source is real, while the discriminator D(G(z)) = 0 for the generated data by G(z).…”

Section: Overview Of Basic Gan Architecturesmentioning

confidence: 99%

Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

Karar¹,

Shouman²,

Chalopin³

2022

Computers, Materials &Amp; Continua

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The novel Coronavirus disease 2019 (COVID-19) pandemic has begun in China and is still affecting thousands of patient lives worldwide daily. Although Chest X-ray and Computed Tomography are the gold standard medical imaging modalities for diagnosing potentially infected COVID-19 cases, applying Ultrasound (US) imaging technique to accomplish this crucial diagnosing task has attracted many physicians recently. In this article, we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images, based on generative adversarial neural networks (GANs). The proposed image classifiers are a semi-supervised GAN and a modified GAN with auxiliary classifier. Each one includes a modified discriminator to identify the class of the US image using semi-supervised learning technique, keeping its main function of defining the "realness" of tested images. Extensive tests have been successfully conducted on public dataset of US images acquired with a convex US probe. This study demonstrated the feasibility of using chest US images with two GAN classifiers as a new radiological tool for clinical check of COVID-19 patients. The results of our proposed GAN models showed that high accuracy values above 91.0% were obtained under different sizes of limited training data, outperforming other deep learning-based methods, such as transfer learning models in the recent studies. Consequently, the clinical implementation of our computer-aided diagnosis of US-COVID-19 is the future work of this study.

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“…According to the aforementioned literature, FCN (Long, Shelhamer & Darrell, 2015) and their improved variants presented accurate segmentation results for both natural or medical images. Therefore, the UNET and variants (Almajalid et al, 2019;Negi et al, 2020), due to its advantages of fast training and high segmentation accuracy are widely used in the field of medical image segmentation.…”

Section: Deep Learning and Medical Image Segmentationmentioning

confidence: 99%

ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans

Raj

Zhu

Khan

et al. 2021

PeerJ Computer Science

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Currently, the new coronavirus disease (COVID-19) is one of the biggest health crises threatening the world. Automatic detection from computed tomography (CT) scans is a classic method to detect lung infection, but it faces problems such as high variations in intensity, indistinct edges near lung infected region and noise due to data acquisition process. Therefore, this article proposes a new COVID-19 pulmonary infection segmentation depth network referred as the Attention Gate-Dense Network- Improved Dilation Convolution-UNET (ADID-UNET). The dense network replaces convolution and maximum pooling function to enhance feature propagation and solves gradient disappearance problem. An improved dilation convolution is used to increase the receptive field of the encoder output to further obtain more edge features from the small infected regions. The integration of attention gate into the model suppresses the background and improves prediction accuracy. The experimental results show that the ADID-UNET model can accurately segment COVID-19 lung infected areas, with performance measures greater than 80% for metrics like Accuracy, Specificity and Dice Coefficient (DC). Further when compared to other state-of-the-art architectures, the proposed model showed excellent segmentation effects with a high DC and F1 score of 0.8031 and 0.82 respectively.

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RDA-UNET-WGAN: An Accurate Breast Ultrasound Lesion Segmentation Using Wasserstein Generative Adversarial Networks

Cited by 62 publications

References 11 publications

Combination of shear wave elastography and BI-RADS in identification of solid breast masses

Combination of shear wave elastography and BI-RADS in identification of solid breast masses

Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans

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