Jian Deng1, Yanyun Ma2, Deng-Ao Li3, Jumin Zhao1, Yi Liu1, Hui Zhang4. 1. College of Information and Computer, Taiyuan University of Technology, Taiyuan, China. 2. Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China. 3. College of Data Science, Taiyuan University of Technology, Taiyuan, China. Electronic address: lidengao@tyut.edu.cn. 4. Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China.. Electronic address: zhanghui_mr@163.com.
Abstract
BACKGROUND AND OBJECTIVE: Breast density (BD) is an independent predictor of breast cancer risk factor. The automatic classification of BD has yet to resolve. In this paper, we propose an improved convolutional neural network (CNN) framework that integrates innovative SE-Attention mechanism to learn discriminative features, aiming for automatic BD classification in mammography. METHODS: A new benchmarking dataset was constructed from 18157 BD images, manually segmented into 4 levels based on Breast Imaging and Reporting Data System (BI-RADS): A (fatty), B (fibro-glandular), C (heterogeneously dense) and D (extremely dense). The proposed method consists of three main phases: (i) data enhancement and normalization of breast images (ii) SE-Attention training for feature re-calibration and fusion to better classify density and (iii) designing the auxiliary loss. We adopt an attention approach where SE-Attention mechanism is used to learn the density features, which is different from previous works. RESULTS: Experimental results demonstrate that the proposed framework obtains higher classification accuracy than the original network, such as Inception-V4, ResNeXt, DenseNet, increasing the performance from 89.97% to 92.17%, 89.64% to 91.57%, 89.20% to 91.79% respectively. Among them, improved Inception-V4 possesses the highest accuracy meanwhile DenseNet improves in the largest extent, both the original and improved methods are more effective than other state-of-the-art image descriptors regarding classification. CONCLUSIONS: We insist that our method will help radiologists provide reliable BD diagnostic services at the expert level, allowing them to focus on patients who are really in need.
BACKGROUND AND OBJECTIVE: Breast density (BD) is an independent predictor of breast cancer risk factor. The automatic classification of BD has yet to resolve. In this paper, we propose an improved convolutional neural network (CNN) framework that integrates innovative SE-Attention mechanism to learn discriminative features, aiming for automatic BD classification in mammography. METHODS: A new benchmarking dataset was constructed from 18157 BD images, manually segmented into 4 levels based on Breast Imaging and Reporting Data System (BI-RADS): A (fatty), B (fibro-glandular), C (heterogeneously dense) and D (extremely dense). The proposed method consists of three main phases: (i) data enhancement and normalization of breast images (ii) SE-Attention training for feature re-calibration and fusion to better classify density and (iii) designing the auxiliary loss. We adopt an attention approach where SE-Attention mechanism is used to learn the density features, which is different from previous works. RESULTS: Experimental results demonstrate that the proposed framework obtains higher classification accuracy than the original network, such as Inception-V4, ResNeXt, DenseNet, increasing the performance from 89.97% to 92.17%, 89.64% to 91.57%, 89.20% to 91.79% respectively. Among them, improved Inception-V4 possesses the highest accuracy meanwhile DenseNet improves in the largest extent, both the original and improved methods are more effective than other state-of-the-art image descriptors regarding classification. CONCLUSIONS: We insist that our method will help radiologists provide reliable BD diagnostic services at the expert level, allowing them to focus on patients who are really in need.
Authors: Shivaji D Pawar; Kamal K Sharma; Suhas G Sapate; Geetanjali Y Yadav; Roobaea Alroobaea; Sabah M Alzahrani; Mustapha Hedabou Journal: Front Public Health Date: 2022-04-25
Authors: Aimilia Gastounioti; Shyam Desai; Vinayak S Ahluwalia; Emily F Conant; Despina Kontos Journal: Breast Cancer Res Date: 2022-02-20 Impact factor: 8.408