Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The radiographic appearance of breast tissue has been established as a strong risk factor for breast cancer. Here  present a complete machine learning framework for automatic estimation of mammographic density (MD) and robust feature extraction for breast cancer risk analysis. Our framework is able to simultaneously classify the breast region, fatty tissue, pectoral muscle, glandular tissue and nipple region. Integral to our method is the extraction of measures of breast density (as the fraction of the breast area occupied by glan- dular tissue) and mammographic pattern. A novel aspect of the segmentation framework is that a probability map associated with the label mask is provided, which indicates the level of confidence of each pixel being classified as the cur- rent label. The Pearson correlation coefficient beten the estimated MD value and the ground truth is 0.8012 (p-value<0.0001).  demonstrate the capability of our methods to discriminate beten women with and without cancer by analyzing the contralateral mammograms of 50 women with unilateral breast cancer, and 50 controls. Using MD  obtained an area under the ROC curve (AUC) of 0.61; hover our texture-based measure of mammographic pattern significantly outperforms the MD discrimination with an AUC of 0.70.
['Xin Chen', 'Emmanouil Moschidis', 'Christopher J. Taylor', 'Susan M. Astley']
I am writing a research paper on a idea Breast Cancer Risk Analysis Based on a Novel Segmentation Framework for Digital Mammograms
[{"from": "human", "value": "I am writing a research paper on a idea Breast Cancer Risk Analysis Based on a Novel Segmentation Framework for Digital Mammograms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The radiographic appearance of breast tissue has been established as a strong risk factor for breast cancer. Here  present a complete machine learning framework for automatic estimation of mammographic density (MD) and robust feature extraction for breast cancer risk analysis. Our framework is able to simultaneously classify the breast region, fatty tissue, pectoral muscle, glandular tissue and nipple region. Integral to our method is the extraction of measures of breast density (as the fraction of the breast area occupied by glan- dular tissue) and mammographic pattern. A novel aspect of the segmentation framework is that a probability map associated with the label mask is provided, which indicates the level of confidence of each pixel being classified as the cur- rent label. The Pearson correlation coefficient beten the estimated MD value and the ground truth is 0.8012 (p-value<0.0001).  demonstrate the capability of our methods to discriminate beten women with and without cancer by analyzing the contralateral mammograms of 50 women with unilateral breast cancer, and 50 controls. Using MD  obtained an area under the ROC curve (AUC) of 0.61; hover our texture-based measure of mammographic pattern significantly outperforms the MD discrimination with an AUC of 0.70."}]