论文标题
一种基于ENA的方法，用于从超声图像中构建乳腺癌识别的深度学习模型
An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images
论文作者
论文摘要
深卷积神经网络（CNN）为图像模式识别提供了一种“端到端”解决方案，在包括医学成像在内的许多应用领域具有令人印象深刻的性能。大多数CNN高性能模型都使用手工制作的网络体系结构，这些网络体系结构需要CNN中的专业知识来利用其潜力。在本文中，我们应用了有效的神经体系结构搜索（ENAS）方法来查找最佳的CNN体系结构，以分类超声（US）图像的乳房病变。我们使用524个美国图像的数据集的实证研究表明，使用ENA生成的最佳模型的平均精度为89.3％，超过了其他手工制作的替代方案。此外，模型的复杂性更简单，更有效。我们的研究表明，CNN模型设计的ENA方法是分类乳腺病变超声图像的有希望的方向。
Deep Convolutional Neural Networks (CNN) provides an "end-to-end" solution for image pattern recognition with impressive performance in many areas of application including medical imaging. Most CNN models of high performance use hand-crafted network architectures that require expertise in CNNs to utilise their potentials. In this paper, we applied the Efficient Neural Architecture Search (ENAS) method to find optimal CNN architectures for classifying breast lesions from ultrasound (US) images. Our empirical study with a dataset of 524 US images shows that the optimal models generated by using ENAS achieve an average accuracy of 89.3%, surpassing other hand-crafted alternatives. Furthermore, the models are simpler in complexity and more efficient. Our study demonstrates that the ENAS approach to CNN model design is a promising direction for classifying ultrasound images of breast lesions.
