关键词: 乳腺影像, 乳腺肿块, 深度学习, 医学图像分类

Abstract: Because the color of X-ray breast mass image is more monotonous than natural images, the edge of the lump in the breast is blurry, and the textures of breast benign and malignant tumors are similar. All these results in the difficulty of classification of X-ray breast mass image. Based on the characteristics of mammography, this paper proposes a method for the classification of X-ray breast mass image based on Convolutional Neural Network（CNN）. The paper has the following contributions. （1）A scheme to extract feature maps of multiple views of breast image is proposed, and the different sizes of convolution kernels are used to extract different granularity in the model, in order to obtain abundant breast image features. （2）The discriminative method is embedded into the optimized model, via designing the new objective function based on the nuance between breast benign and malignant tumors, and the classification error is therefore amplified to enlarge penalties, further the model pattern can evolved to the smallest error direction. The proposed method is trained on the open data set. Cross validation, indicates that the proposed method can achieve AUC of 0.712 9, which is superior to the state-of-the-art classification algorithm of X-ray breast mass image, and the robustness of the proposed method is stronger.

Key words: breast imaging, breast tumors, deep learning, medical image classification