Artificial Intelligence Medical Ultrasound Equipment: Application of Breast Lesions Detection. (July 2020)

• Record Type: Journal Article
• Authors: Zhang, Xuesheng; Lin, Xiaona; Zhang, Zihao; Dong, Licong; Sun, Xinlong; Sun, Desheng; Yuan, Kehong
• Is Part Of: Ultrasonic imaging. Volume 42:Number 4/5(2020)
• Page Start: 191
• Page End: 202
• Publication Date: 2020-07
• Subjects: artificial intelligence algorithm -- medical ultrasound equipment -- breast lesions -- lightweight neural network -- real-time detection -- knowledge distillation; Diagnostic ultrasonic imaging -- Methodology -- Periodicals; Ultrasonic testing -- Periodicals; Ultrasonic imaging -- Periodicals; Ultrasonography -- Periodicals; Échographie -- Méthodologie -- Périodiques; Essais par ultrasons -- Périodiques; Imagerie ultrasonore -- Périodiques; 616.07543
• DOI: 10.1177/0161734620928453
• Languages: English
• ISSNs: 0161-7346

Abstract

Breast cancer ranks first among cancers affecting women's health. Our work aims to realize the intelligence of the medical ultrasound equipment with limited computational capability, which is used for the assistant detection of breast lesions. We embed the high-computational deep learning algorithm into the medical ultrasound equipment with limited computational capability by two techniques: (1) lightweight neural network: considering the limited computational capability of ultrasound equipment, a lightweight neural network is designed, which greatly reduces the amount of calculation. And we use the technique of knowledge distillation to train the low-precision network helped with the high-precision network; (2) asynchronous calculations: consider four frames of ultrasound images as a group; the image of the first frame of each group is used as the input of the network, and the result is respectively fused with the images of the fourth to seventh frames. An amount of computation of 30 GFLO/frame is required for the proposed lightweight neural network, about 1/6 of that of the large high-precision network. After trained from scratch using the knowledge distillation technique, the detection performance of the lightweight neural network (sensitivity = 89.25%, specificity = 96.33%, the average precision [AP] = 0.85) is close to that of the high-precision network (sensitivity = 98.3%, specificity = 88.33%, AP = 0.91). By asynchronous calculation, we achieve real-time automatic detection of 24 fps (frames per second) on the ultrasound equipment. Our work proposes a method to realize the intelligence of the low-computation-power ultrasonic equipment, and successfully achieves the real-time assistant detection of breast lesions. The significance of the study is as follows: (1) The proposed method is of practical significance in assisting doctors to detect breast lesions; (2) our method provides some practical and theoretical support for the development and engineering of intelligent equipment based on artificial intelligence algorithms.