Authors:
Yiming Bian
and
Arun K. Somani
Affiliation:
Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa, U.S.A.
Keyword(s):
Noise Training, Single-noise Training, Mix-noise Training, Breast Ultrasound Image, Image Classification.
Abstract:
Breast cancer is one of the most common and deadly diseases. An early diagnosis is critical and in-time treatment can help prevent the further spread of cancer. Breast ultrasound images are widely used for diagnosis, but the diagnosis heavily depends on the radiologist’s expertise and experience. Therefore, computer-aided diagnosis (CAD) systems are developed to provide an effective, objective, and reliable understanding of medical images for radiologists and diagnosticians. With the help of modern convolutional neural networks (CNNs), the accuracy and efficiency of CAD systems are greatly improved. CNN-based methods rely on training with a large amount of high-quality data to extract the key features and achieve a good performance. However, such noise-free medical data in high volume are not easily accessible. To address the data limitation, we propose a novel two-stage noise training methodology that effectively improves the performance of breast ultrasound image classification wit
h speckle noise. The proposed mix-noise-trained model in Stage II trains on a mix of noisy images at multiple different intensity levels. Our experiments demonstrate that all tested CNN models obtain resilience to speckle noise and achieve excellent performance gain if the mix proportion is selected appropriately. We believe this study will benefit more people with a faster and more reliable diagnosis.
(More)