A Deep Learning-based Computer-aided Diagnosis System for Mammographic Lesion Detection
Shintaro Suzuki, Xiaoyong Zhang, Noriyasu Homma, Kei Ichiji, Yumi Takane, Satoru Yanagaki, Yusuke Kawasumi, Tadashi Ishibashi, Makoto Yoshizawa · Transactions of the Society of Instrument and Control Engineers · 2018
In recent years, deep convolutional neural network (DCNN) has widely been applied for image recognition, and shown a remarkable performance in various natural image-related applications. However, for medical image-related application such as computer-aided diagnosis (CAD), due to the limitation of training data and the modality difference between the natural and medical images, training the DCNN for medical image recognition is still a research topic. In this paper, we propose a DCNN-based method for lesion detection in mammograms. The proposed method consists of the following two steps. Given a mammogram, lesion candidates are firstly detected from the mammogram based on their intensity characteristics. Secondly, a transfer learning-based method is applied for training an existing DCNN to classify the lesion candidates into lesions or normal tissues. The proposed method is tested on a public mammogram database. Compared with several previous studies, our proposed method achieved a higher true positive rate and a lower false positive in lesion detection.