Combined adaptive enhancement and regionâ growing segmentation of breast masses on digitized mammograms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134789/1/mp8658.pd

Linear motion correction in three dimensions applied to dynamic gadolinium enhanced breast imaging

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134776/1/mp8576.pd

Outcome of Men Presenting with Clinical Breast Problems: The Role of Mammography and Ultrasound

The purpose of this study was to determine the outcome of men presenting with clinical breast problems for breast imaging and to evaluate the role of mammography and ultrasound in the diagnosis of benign and malignant breast problems. We retrospectively reviewed clinical, radiographic, and pathologic records of 165 consecutive symptomatic men presenting to Breast Imaging over a 4 year period. We assessed the clinical indication for referral, mammographic findings, sonographic findings, histologic results, and clinical outcomes. Patients ranged in age from 22 to 96 years. Breast Imaging Reporting and Data System (BI-RADS) category 4 and 5 mammograms and solid sonographic masses were considered suspicious for malignancy. Six of 165 men (4%) had primary breast carcinoma, which were mammographically suspicious in all 6 (100%). Five were invasive ductal carcinoma and one was ductal carcinoma in situ (DCIS). Of 164 mammograms, 20 (12%) were suspicious. Six were cancer and 14 were benign. Clinical follow-up for 2 years or biopsy results were available for 138 of the 165 men (84%). Twelve with benign mammographic findings had benign biopsies. All men with benign mammography not undergoing biopsy were cancer free. Sensitivity for cancer detection (mammography) was 100% and specificity was 90%. Positive predictive value (mammography) was 32% (6 of 19) and the negative predictive value was 100%. Sonography was performed in 68 of the 165 men (41%). Three of three cancers (100%) were solid sonographic masses. There were 9 of 68 false-positive examinations (13%). Sensitivity and negative predictive value for cancer detection (ultrasound) was 100% and specificity was 74%. The most common clinical indication for referral was mass/thickening (56%). Mammography had excellent sensitivity and specificity for breast cancer detection and should be included as the initial imaging examination of men with clinical breast problems. The negative predictive value of 100% for mammography suggests that mammograms read as normal or negative need no further examination if the clinical findings are not suspicious. A normal ultrasound in these men confirms the negative predictive value of a normal mammogram.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74705/1/j.1075-122X.2006.00298.x.pd

Improvement of computerized mass detection on mammograms: Fusion of twoâ view information

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135080/1/mp6098.pd

Automated registration of breast lesions in temporal pairs of mammograms for interval change analysisâ local affine transformation for improved localization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134991/1/mp6134.pd

Comparison of similarity measures for the task of template matching of masses on serial mammograms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134879/1/mp1892.pd

Analysis of temporal changes of mammographic features: Computerâ aided classification of malignant and benign breast masses

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135117/1/mp2242.pd

Design and evaluation of an external filter technique for exposure equalization in mammography

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134768/1/mp8659.pd

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135545/1/mp7345_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135545/2/mp7345.pd

Automated pectoral muscle identification on MLOâ view mammograms: Comparison of deep neural network to conventional computer vision

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149204/1/mp13451_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149204/2/mp13451.pd