3 March 2017 A novel deep learning-based approach to high accuracy breast density estimation in digital mammography
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Abstract
Mammographic breast density is a well-established marker for breast cancer risk. However, accurate measurement of dense tissue is a difficult task due to faint contrast and significant variations in background fatty tissue. This study presents a novel method for automated mammographic density estimation based on Convolutional Neural Network (CNN). A total of 397 full-field digital mammograms were selected from Seoul National University Hospital. Among them, 297 mammograms were randomly selected as a training set and the rest 100 mammograms were used for a test set. We designed a CNN architecture suitable to learn the imaging characteristic from a multitudes of sub-images and classify them into dense and fatty tissues. To train the CNN, not only local statistics but also global statistics extracted from an image set were used. The image set was composed of original mammogram and eigen-image which was able to capture the X-ray characteristics in despite of the fact that CNN is well known to effectively extract features on original image. The 100 test images which was not used in training the CNN was used to validate the performance. The correlation coefficient between the breast estimates by the CNN and those by the expert’s manual measurement was 0.96. Our study demonstrated the feasibility of incorporating the deep learning technology into radiology practice, especially for breast density estimation. The proposed method has a potential to be used as an automated and quantitative assessment tool for mammographic breast density in routine practice.
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Chul Kyun Ahn, Chul Kyun Ahn, Changyong Heo, Changyong Heo, Heongmin Jin, Heongmin Jin, Jong Hyo Kim, Jong Hyo Kim, } "A novel deep learning-based approach to high accuracy breast density estimation in digital mammography", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342O (3 March 2017); doi: 10.1117/12.2254264; https://doi.org/10.1117/12.2254264
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