GAN-based data augmentation to improve breast Ultrasound and Mammography Mass Classification

Published: 22 January 2024| Version 1 | DOI: 10.17632/wxz74j8bts.1
Contributors:
Yuliana Jimenez,
,
,

Description

Various GAN methods, including Wasserstein GAN with Gradient Penalty (WGAN-GP), Cycle GAN, Conditional GAN, and Spectral Normalization GAN (SNGAN), were tested for data augmentation in breast regions of interest (ROIs) using mammography and ultrasound databases. The study employed real, synthetic, and hybrid ROIs (128x128 pixels) to train a Resnet network for classifying as benign (B) or malignant (M) classes. The quality and diversity of the synthetic data were assessed using several metrics: Fréchet Inception Distance (FID), Kernel Inception Distance (KID), Structural Similarity Index (SSIM), Multi-Scale SSIM (MS-SSIM), Blind Reference Image Spatial Quality Evaluator (BRISQUE), Naturalness Image Quality Evaluator (NIQE), and Perception-based Image Quality Evaluator (PIQE).Results revealed that the SNGAN model (FID=52.89) was most effective for augmenting mammography data, while CGAN (FID=116.03) excelled with ultrasound data. Cycle GAN and WGAN-GP, though demonstrating lower KID values, did not perform better than SNGAN and CGAN. The lower average MS-SSIM values suggested that SNGAN and CGAN produced a high diversity of synthetic images. However, lower SSIM, BRISQUE, NIQE, and PIQE values indicated poor quality in both real and synthetic images. Classification results showed high accuracy without data augmentation in both US (93.1%B/94.9%M) and mammography (80.9%B/76.9%M).

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Steps to reproduce

1. RoIs Segmentation 2. RoIs Data augmentation, GANs algorithms 2.1 Evaluation metrics FID, KID, SSIM,MSSIM, BRISQE,PIQE,NIQUE 3. RoIs classification, Resnet Algorithm 3.1Evaluation metrics

Institutions

University of Waterloo, Universitat Politecnica de Valencia Institut de Matematica Multidisciplinar, Universidad Tecnica Particular de Loja

Categories

Breast Cancer, Deep Learning, Data Augmentation

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