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.

中文翻译：

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人工智能医疗超声设备：乳腺病变检测的应用

在影响女性健康的癌症中，乳腺癌居首位。我们的工作旨在实现计算能力有限的医用超声设备的智能化，用于乳腺病变的辅助检测。我们通过两种技术将高计算深度学习算法嵌入到计算能力有限的医用超声设备中：（1）轻量级神经网络：考虑到超声设备计算能力有限，设计了一种轻量级神经网络，大大降低了计算量的计算。并且我们使用知识蒸馏的技术来训练在高精度网络的帮助下的低精度网络；(2)异步计算：将四帧超声图像视为一组；将每组第一帧的图像作为网络的输入，将结果分别与第四到第七帧的图像进行融合。所提出的轻量级神经网络需要 30 GFLO/frame 的计算量，约为大型高精度网络的 1/6。使用知识蒸馏技术从头开始训练后，轻量级神经网络的检测性能（灵敏度 = 89.25%，特异性 = 96.33%，平均精度 [AP] = 0.85）接近于高精度网络（灵敏度= 98.3%，特异性 = 88.33%，AP = 0.91）。通过异步计算，我们在超声设备上实现了24 fps（每秒帧数）的实时自动检测。我们的工作提出了一种实现低计算功率超声设备智能化的方法，并成功实现了乳腺病变的实时辅助检测。研究意义如下：（1）所提出的方法对辅助医生检测乳腺病变具有实际意义；(2)我们的方法为基于人工智能算法的智能装备的开发和工程提供了一定的实践和理论支持。