Prior-guided generative adversarial network for mammogram synthesis

Deep Learning is vital in medical imaging solutions and clinical applications. However, multiple reasons, such as data scarcity and imbalance in the medical image dataset, cause performance issues in various deep learning models. Thus, generating synthetic medical data close to real images is an immediate need of time. The Mammographic Image Analysis Society (MIAS) dataset, the standard dataset for studying breast anomalies, suffers from a class imbalance problem as very few images correspond to malignant and benign cases are there compared to normal cases. This paper proposes a data augmentation model based on Generative Adversarial Network (GAN) architecture to generate synthetic mammograms of different cases, such as normal, benign, and malignant. The proposed method’s novelty lies in its capability of generating multiple variants of a class-labelled mammogram, which is more realistic in conserving the adherent breast tissue characteristics. These synthetic mammograms are a useful solution for resolving class imbalance problems. Generated samples of each class are added to the MIAS dataset to address the class imbalance problem. This study also demonstrates that the augmented dataset with cGAN-generated images has enhanced the 2-class- and 3-class breast cancer classification performance by 3.9%. The proposed cGAN model is efficient in capturing the features of abnormalities. The 90% of class-labelled mammograms generated by cGAN belong to the same class in the case of real medical data. The proposed work exhibits the generation of synthetic mammograms using a GAN-based approach. Augmentation of the training dataset with GAN-generated images significantly enhances breast image classification. [Display omitted] •Create realistic synthetic mammograms from real mammograms•Provide a solution to generate class-specific images to solve the class imbalance problem in many public datasets•A data augmentation model is proposed to improve the training dataset