In medical image analysis, deep learning has great application potential. Discovering a method for extracting valuable information from medical images and integrating that information closely with medical treatment has recently become a major topic of interest. Because obtaining large volumes of breast lesion ultrasound image data is difficult, transfer learning is usually employed to obtain benign and malignant classification of breast lesions. However, because of blurred unclear regions of interest in breast lesion ultrasound images and severe speckle noise interference, convolutional neural networks have proven ineffective in extracting features, thus providing unreliable classification results.

中文翻译：

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基于图像分解和转移学习的乳房超声病变分类

在医学图像分析中，深度学习具有巨大的应用潜力。发现一种从医学图像中提取有价值的信息并将该信息与医学紧密结合的方法近来已成为人们关注的主要主题。因为很难获得大量的乳房病变超声图像数据，所以通常采用转移学习来获得乳房病变的良性和恶性分类。但是，由于乳房病变超声图像中模糊的关注区域不清晰以及严重的斑点噪声干扰，卷积神经网络已被证明在提取特征方面无效，因此无法提供可靠的分类结果。