Mr. Basel Alyafi Profile

Basel Alyafi

Publications (2)

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Proceedings Article | 22 May 2020 Paper

Quality analysis of DCGAN-generated mammography lesions

Basel Alyafi, Oliver Diaz, Premkumar Elangovan, Joan Vilanova, Javier del Riego, Robert Marti

Proceedings Volume 11513, 115130B (2020) https://doi.org/10.1117/12.2560473

KEYWORDS: Mammography, Image processing, Tissues, Breast, Data modeling, Visualization, Databases, Medical imaging

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Medical image synthesis has gained a great focus recently, especially after the introduction of Generative Adversarial Networks (GANs). GANs have been used widely to provide anatomically-plausible and diverse samples for augmentation and other applications, including segmentation and super resolution. In our previous work, Deep Convolutional GANs were used to generate synthetic mammogram lesions, masses mainly, that could enhance the classification performance in imbalanced datasets. In this new work, a deeper investigation was carried out to explore other aspects of the generated images evaluation, i.e., realism, feature space distribution, and observer studies. t-Stochastic Neighbor Embedding (t-SNE) was used to reduce the dimensionality of real and fake images to enable 2D visualisations. Additionally, two expert radiologists performed a realism-evaluation study. Visualisations showed that the generated images have a similar feature distribution of the real ones, avoiding outliers. Moreover, the Receiver Operating Characteristic (ROC) study showed that the radiologists could not, in many cases, distinguish between synthetic and real lesions, giving accuracies between 51% and 59% using a balanced sample set.

Proceedings Article | 16 March 2020 Paper

DCGANs for realistic breast mass augmentation in x-ray mammography

Basel Alyafi, Oliver Diaz, Robert Marti

Proceedings Volume 11314, 1131420 (2020) https://doi.org/10.1117/12.2543506

KEYWORDS: Breast, Mammography, Tissues, Breast cancer, X-rays, Computer aided diagnosis and therapy, Cancer, Image processing, Databases

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Early detection has a major contribution to the curability of breast cancer, and using mammographic images, this can be achieved non-invasively. Supervised deep learning, the dominant computer-aided detection (CADe) tool currently, has played a great role in object detection in computer vision, but it suffers from a limiting property: the need of a large amount of labelled data. This becomes stricter when it comes to medical datasets which require high-cost and time-consuming annotations. Furthermore, medical datasets are usually imbalanced, a condition that often hinders classifiers performance. The aim of this paper is to learn the distribution of the minority class to synthesise new samples in order to improve lesion detection in mammography. Deep Convolutional Generative Adversarial Networks (DCGANs) can efficiently generate breast masses. They were trained on increasing-size subsets of a mammographic dataset and used to generate diverse and realistic breast masses. The effect of including the generated images and/or applying horizontal and vertical flipping was tested in an environment where an imbalanced dataset (ratio of 1:10) of masses and normal tissue patches was classified using a fully- convolutional network. A maximum of 0:09 improvement of F1 score was reported by using DCGANs along with flipping augmentation in contrast to using the original images. We show that DCGANs can be used for synthesising photo-realistic breast mass patches with a considerable diversity. It is demonstrated that appending synthetic images in this environment, along with flipping, outperforms the traditional augmentation method of flipping solely, offering faster improvements as a function of the training set size.

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