Relevant bibliographies by topics / Mammogram

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Mammogram'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Mammogram"

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Hegar, Veronica, Kristin Oliveira, Bharat Kakarala, Alicia Mangram, and Ernest Dunn. "Annual Mammography Screening: Is it Necessary?" American Surgeon 78, no. 1 (2012): 104–6. http://dx.doi.org/10.1177/000313481207800145.

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Recent recommendations from the U.S. Preventative Services Task Force suggest that screening mammography for women should be biennial starting at age 50 years and continue to age 74 years. With these recommendations in mind, we proposed a study to evaluate women at our institution in whom breast cancer is diagnosed within 1 year of a previously benign mammogram. A retrospective chart review was performed over a 4-year period. Only patients who had both diagnostic mammograms and previous mammograms performed at our institution and a pathologic diagnosis of breast cancer were included. Benign ma
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Wen, Xuesong, Jianjun Li, and Liyuan Yang. "Breast Cancer Diagnosis Method Based on Cross-Mammogram Four-View Interactive Learning." Tomography 10, no. 6 (2024): 848–68. http://dx.doi.org/10.3390/tomography10060065.

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Computer-aided diagnosis systems play a crucial role in the diagnosis and early detection of breast cancer. However, most current methods focus primarily on the dual-view analysis of a single breast, thereby neglecting the potentially valuable information between bilateral mammograms. In this paper, we propose a Four-View Correlation and Contrastive Joint Learning Network (FV-Net) for the classification of bilateral mammogram images. Specifically, FV-Net focuses on extracting and matching features across the four views of bilateral mammograms while maximizing both their similarities and dissim
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Oza, Parita Rajiv, Paawan Sharma, and Samir Patel. "Transfer Learning Assisted Classification of Artefacts Removed and Contrast Improved Digital Mammograms." Scalable Computing: Practice and Experience 23, no. 3 (2022): 115–27. http://dx.doi.org/10.12694/scpe.v23i3.1992.

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Mammograms are essential radiological images used to diagnose breast cancer well in advance. However, an accurate diagnosis also depends on the quality of mammogram images. Therefore, removal of artefacts and mammogram enhancement are necessary pre-processing steps. Artefact removal helps exclude unsolicited regions in the mammograms and limits the search for suspicious regions without excessive impact from the background. Mammogram enhancements improve apparent visual details and improve some features of an image. In this paper, we propose a method for mammogram pre-processing. These pre-proc
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Manik, Ritika, Connor B. Grady, Sara Ginzberg, et al. "Racial disparities in diagnostic follow-up following BIRADS 0 mammogram." Journal of Clinical Oncology 40, no. 16_suppl (2022): 6559. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.6559.

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6559 Background: Delays in follow-up after abnormal mammograms can lead to worse outcomes and may contribute to health disparities. BIRADS 0 mammograms necessitate additional diagnostic imaging, and BIRADS 4 or 5 mammograms should be followed by biopsy. The goal of this study was to investigate racial disparities in rates and timeliness of (1) diagnostic follow-up after a BIRADS 0 screening mammogram and (2) biopsy following a subsequent BIRADS 4 or 5 diagnostic mammogram. Methods: We included women ≥18 years old who underwent a screening mammogram at the Hospital of the University of Pennsylv
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Zhong, Yang Jun, and Qian Cai. "A Novel Registration Approach for Mammograms Based on SIFT and Graph Transformation." Applied Mechanics and Materials 157-158 (February 2012): 1313–19. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1313.

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Mammogram registration is an important step in the processing of automatic detection of breast cancer. It provides aid to better visualization correspondence on temporal pairs of mammograms. This paper presents a novel algorithm based on SIFT feature and Graph Transformation methods for mammogram registration. First, features are extracted from the mammogram images by scale invariant feature transform (SIFT) method. Second, we use graph transformation matching (GTM) approach to obtain more accurate image information. At last, we registered a pair of mammograms using Thin-Plate spline (TPS) int
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Biggs, MJP, and D. Ravichandran. "Mammography in Symptomatic Women Attending a Rapid Diagnosis Breast Clinic: A Prospective Study." Annals of The Royal College of Surgeons of England 88, no. 3 (2006): 306–8. http://dx.doi.org/10.1308/003588406x98603.

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INTRODUCTION We determined whether it is safe to avoid mammograms in a group of symptomatic women with a non-suspicious history and clinical examination. PATIENTS AND METHODS Symptomatic women aged 35 years or over newly referred to a rapid-diagnosis breast clinic underwent mammography on arrival in the clinic. A breast radiologist reported on the mammograms. An experienced clinician who was unaware of the mammogram findings examined patients and decided whether a mammogram was indicated or not. If not, a management plan was formulated. Mammogram findings were then provided to the clinician an
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Abdullah, Nasturah, Noorhida Baharudin, Mariam Mohamad, and Mohamed-Syarif Mohamed-Yassin. "Factors Associated with Screening Mammogram Uptake among Women Attending an Urban University Primary Care Clinic in Malaysia." International Journal of Environmental Research and Public Health 19, no. 10 (2022): 6103. http://dx.doi.org/10.3390/ijerph19106103.

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Screening mammograms have resulted in a reduction in breast cancer mortality, yet the uptake in Malaysia was low. This study aimed to determine the prevalence and factors associated with screening mammogram uptake among women attending a Malaysian primary care clinic. A cross-sectional study was conducted among 200 women aged 40 to 74 attending the clinic. The data was collected using questionnaires assessing sociodemographic, clinical characteristics, knowledge and health beliefs. Multiple logistic regression was used to identify factors associated with mammogram uptake. The prevalence of scr
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Howard, Daniel, Simon C. Roberts, Conor Ryan, and Adrian Brezulianu. "Textural Classification of Mammographic Parenchymal Patterns with the SONNET Selforganizing Neural Network." Journal of Biomedicine and Biotechnology 2008 (2008): 1–11. http://dx.doi.org/10.1155/2008/526343.

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In nationwide mammography screening, thousands of mammography examinations must be processed. Each consists of two standard views of each breast, and each mammogram must be visually examined by an experienced radiologist to assess it for any anomalies. The ability to detect an anomaly in mammographic texture is important to successful outcomes in mammography screening and, in this study, a large number of mammograms were digitized with a highly accurate scanner; and textural features were derived from the mammograms as input data to a SONNET selforganizing neural network. The paper discusses h
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Palos, Guadalupe R., Katherine Gilmore, Patricia Chapman, Weiqi Bi, Paula Lewis-Patterson, and Alma Maria Rodriguez. "HSR19-104: Patterns of Providers’ Mammogram Referrals for Asymptomatic Breast Cancer Survivors." Journal of the National Comprehensive Cancer Network 17, no. 3.5 (2019): HSR19–104. http://dx.doi.org/10.6004/jnccn.2018.7227.

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Background: NCCN Guidelines recommend annual mammography for surveillance in asymptomatic women diagnosed with breast cancer. There is limited evidence to support which type of mammogram—screening or diagnostic—should be ordered for asymptomatic breast cancer survivors. Our objective was to assess the referral patterns for mammograms in 2 breast clinics: survivorship (SC) and oncology (OC). Methods: A retrospective analysis of institutional databases was conducted to identify a convenience sample of women with limited invasive breast cancer who were (1) alive 5 years post-treatment and (2) see
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Eberl, M., G. Broffman, J. Pomerantz, et al. "Linked claims and medical records for case management of breast cancer diagnosis." Journal of Clinical Oncology 24, no. 18_suppl (2006): 6000. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.6000.

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6000 Background: Real time linkage of medical records and administrative claims may assist physicians in managing cancer care. Failure to obtain requisite follow-up (f/u) of abnormal mammograms may delay cancer diagnosis and impact outcome with studies showing that up to 20% of women do not get recommened f/u. We tested the accuracy of claims data linked to medical records in identifying women who did not receive f/u for an abnormal mammogram (BI-RADS 0,3,4,5). Methods: Electronic medical records in a staff model practice affiliated with a single payer were scanned to identify the BI-RADS code
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Dissertations / Theses on the topic "Mammogram"

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Carlsson, Hampus. "Fully Convolutional Networks for Mammogram Segmentation." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158127.

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Segmentation of mammograms pertains to assigning a meaningful label to each pixel found in the image. The segmented mammogram facilitates both the function of Computer Aided Diagnosis Systems and the development of tools used by radiologists during examination. Over the years many approaches to this problem have been presented. A surge in the popularity of new methods to image processing involving deep neural networks present new possibilities in this domain, and this thesis evaluates mammogram segmentation as an application of a specialized neural network architecture, U-net. Results are prod
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Riley, Graeme Alexander. "Optimised mammogram displays for improved breast cancer detection." Thesis, University of Plymouth, 2016. http://hdl.handle.net/10026.1/5150.

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In current mammography practice, radiologists typically view mammograms in a symmetric, side-by-side, configuration in the belief that abnormalities will be made salient because they break the perceived symmetry. The literature on the use of symmetry as an aid to signal detection is limited and this thesis has taken a psychophysical approach to investigate the radiologist’s task of detecting a small mass (a blob) in paired mammogram backgrounds. Initial experiments used Gaussian white noise and synthetic mammogram backgrounds to test observer performance for the radiologist’s task using symmet
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Stewart, Brian D. "Automatic mammogram interpretation using knowledge-based computer vision." Thesis, University of Dundee, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333875.

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Copeland, Valerie Anne. "Factors that influence follow-up after an abnormal mammogram." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1130.

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Kok-Wiles, Siewli. "Comparing mammogram pairs in the detection of mammographic lesions." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298421.

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Majeed, Taban Fouad. "Segmentation, super-resolution and fusion for digital mammogram classification." Thesis, University of Buckingham, 2016. http://bear.buckingham.ac.uk/162/.

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Mammography is one of the most common and effective techniques used by radiologists for the early detection of breast cancer. Recently, computer-aided detection/diagnosis (CAD) has become a major research topic in medical imaging and has been widely applied in clinical situations. According to statics, early detection of cancer can reduce the mortality rates by 30% to 70%, therefore detection and diagnosis in the early stage are very important. CAD systems are designed primarily to assist radiologists in detecting and classifying abnormalities in medical scan images, but the main challenges hi
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Alshabibi, Abdulaziz Saad Abdullah. "Factors Causing Variability When Reading Mammograms." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29415.

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Aims: This thesis investigated the impacts on radiologist performance of 1) the time of day at which an image is read, 2) how long a radiologist has been awake and the hours slept the night before and 3) the number of cases read without a break. Studies 2 and 3 also examined whether the influence of the studied factors varied according to reader experience. Methods: Data were collected during conference workshops, and radiologist accuracy was evaluated with BreastScreen Reader Assessment Strategy test sets, each containing 60 digital mammography cases. In study 1, 197 reader assessments wer
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Azevedo, Roger. "Expert problem solving in mammogram interpretation, a visual cognitive task." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0002/NQ44353.pdf.

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McLoughlin, Kirstin J. "Computer aided detection of microcalcification clusters in digital mammogram images." Thesis, University of Canterbury. Electrical and Computer Engineering, 2004. http://hdl.handle.net/10092/6536.

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Recent advancements in computer technology have ensured that early detection of breast cancer, via computer aided detection (CAD) schemes, has become a rapidly expanding field of research. There is a desire to improve the detection accuracy of breast cancer without increasing the number of falsely identified cancers. The CAD scheme considered here is intended to assist radiologists in the detection of micro calcification clusters, providing a real contribution to the mammography screening process. Factors that affect the detection accuracy of micro calcifications in digital mammograms include
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Kulkarni, Pradnya. "Pixel N-grams for Mammographic Image Classification." Thesis, Federation University Australia, 2017. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160424.

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X-ray screening for breast cancer is an important public health initiative in the management of a leading cause of death for women. However, screening is expensive if mammograms are required to be manually assessed by radiologists. Moreover, manual screening is subject to perception and interpretation errors. Computer aided detection/diagnosis (CAD) systems can help radiologists as computer algorithms are good at performing image analysis consistently and repetitively. However, image features that enhance CAD classification accuracies are necessary for CAD systems to be deployed. Many CAD syst
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Books on the topic "Mammogram"

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Illinois. Vehicle Services Department. Mammogram license plates. Jesse White, Secretary of State [Vehicle Services Dept.], 2003.

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Illinois. Vehicle Services Dept. Mammogram license plates. Jesse White, Secretary of State [Vehicle Services Dept., 2007.

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New York (N.Y.). City Council. Make mammogram appointments more accessible. The Council of the City of New York, 2002.

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Bhateja, Vikrant, Mukul Misra, and Shabana Urooj. Non-Linear Filters for Mammogram Enhancement. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0442-6.

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National Cancer Institute (U.S.). Cancer Information Service, ed. Make and keep your mammogram appointment. National Cancer Institute, Cancer Information Service, 2002.

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National Cancer Institute (U.S.), ed. Breast cancer: Do the right thing : get a mammogram. National Cancer Institute, 1990.

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National Institutes of Health (U.S.), ed. Are you age 50 or over?: A mammogram could save your life. National Institutes of Health, National Cancer Institute, 1994.

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Parker, James N., and Philip M. Parker. Mammogram: A medical dictionary, bibliography, and annotated research guide to Internet references. ICON Health Publications, 2004.

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National Cancer Institute (U.S.), ed. Chances are-- you need a mammogram: A guide for midlife and older women. National Institutes of Health, National Cancer Institute, 1996.

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Lanyi, Marton. Brustkrankheiten im Mammogramm. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-06177-0.

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Book chapters on the topic "Mammogram"

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Godellas, Constantine V. "Abnormal Mammogram." In Common Surgical Diseases. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2945-0_89.

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Kopkash, Katherine. "Abnormal Mammogram." In Common Surgical Diseases. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1565-1_62.

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Yaghmai, Nazanin, Tiffany Yu, Regan Ferraro, and Guita Rahbar. "Screening Mammogram." In Absolute Breast Imaging Review. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08274-0_3.

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Mancinelli, Jenna, and Adrian Lopez. "Abnormal Mammogram." In Passing the General Surgery Oral Board Exam. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-78244-2_2.

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Bhateja, Vikrant, Mukul Misra, and Shabana Urooj. "Mammogram Benchmarking Databases." In Non-Linear Filters for Mammogram Enhancement. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0442-6_11.

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Grigorian, Areg, Christian de Virgilio, and Danielle M. Hari. "Abnormal Screening Mammogram." In Surgery. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05387-1_5.

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Grigorian, Areg, Christian de Virgilio, and Danielle M. Hari. "Abnormal Screening Mammogram." In Surgery. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-1726-6_4.

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Chan, Tiffany L., Tiffany Yu, and Irene Tsai. "Diagnostic Mammogram and Ultrasound." In Absolute Breast Imaging Review. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08274-0_4.

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Karaman, Batuhan K., Katerina Dodelzon, Gozde B. Akar, and Mert R. Sabuncu. "Longitudinal Mammogram Risk Prediction." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72086-4_41.

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Bhateja, Vikrant, Mukul Misra, and Shabana Urooj. "Mammogram Enhancement and Associated Challenges." In Non-Linear Filters for Mammogram Enhancement. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0442-6_4.

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Conference papers on the topic "Mammogram"

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Swathi, K. S., T. P. Channabasavaraju, S. R. Sujatha, and S. Renukalatha. "Breast Cancer Classification On Mammogram Images." In 2025 3rd International Conference on Smart Systems for applications in Electrical Sciences (ICSSES). IEEE, 2025. https://doi.org/10.1109/icsses64899.2025.11009934.

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Razgallah, Hichem, and Jiyun Li. "Classification of Mammogram Analysis Using BL-CNN." In 2024 7th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2024. https://doi.org/10.1109/prai62207.2024.10826701.

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Gielo Perczak, Krystyna, Riley Mcnaboe, and Hugo Posada-quintero. "Evaluation of Patient Stress During Mammograms through Surface Electromyography Analysis." In 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004842.

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Regular mammograms are recommended for women to allow for early detection of breast cancer and in turn, proper treatment and improved prognosis of patients. However, the stress and discomfort associated with the procedure deter many women from routine screening. Most previous work attempting to characterize this pain utilizes subjective, questionnaire-based methods. The variability in methodology and subjectivity of these approaches requires a more objective strategy to fully understand mammogram related stress. Bio signals such as surface electromyography (sEMG) have been increasing in popula
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Clarke, Nicole, and Tara Pellegrino. "Investigating Patient Barriers in Receiving Mammography Screenings Following the COVID-19 Pandemic." In 28th Annual Rowan-Virtua Research Day. Rowan University Libraries, 2024. http://dx.doi.org/10.31986/issn.2689-0690_rdw.stratford_research_day.23_2024.

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Current literature shows reduced breast cancer mortality is associated with compliance of mammographic screenings (Duffy, 2020), specifically increasing the survival rate an estimated 1-2% every year for the past thirty years (Giordano, 2004). However, during the Covid-19 pandemic, rates of basic cancer screenings, including breast cancer, all declined (Chen, 2021). Unfortunately, even in a “post-pandemic” world, years after the height of the pandemic, the Rowan-Virtua School of Osteopathic Medicine Family Medicine Office has noticed many female patients still have not kept up with their routi
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Suresh, R., A. Nagaraja Rao, and B. Eswara Reddy. "Improving the mammogram images by intelligibility mammogram enhancement method." In 2018 2nd International Conference on Inventive Systems and Control (ICISC). IEEE, 2018. http://dx.doi.org/10.1109/icisc.2018.8398954.

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Amreen, Saika, and Nithya Vidyaprakash. "Mammogram blind spots." In Radiopaedia 2023. Radiopaedia.org, 2023. http://dx.doi.org/10.53347/rposter-1707.

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Duan Zhu, Tian Hong, and Sun Lei. "Research on mammogram enhancement." In 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccsit.2010.5564127.

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Muttarak, M. "The false negative mammogram." In Asian Breast Diseases Association (ABDA) 3rd Teaching Course: Advances in the Management of Breast Diseases. Asian Breast Diseases Association, 2005. http://dx.doi.org/10.2349/biij.1.1.e6-29.

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Samant, Neha, and Poonam Sonar. "Mammogram Classification in Transform Domain." In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2018. http://dx.doi.org/10.1109/spin.2018.8474186.

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Lochanambal, K. P., and M. Karnan. "Hybrid heuristics for mammogram segmentation." In 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2010. http://dx.doi.org/10.1109/iccic.2010.5705894.

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Reports on the topic "Mammogram"

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Cholly, Kristine, Marcus Barksdale, Rachel Gates, Mabel Olaya, and Laura Reed. Written Reminders for Mammogram Compliance. University of Tennessee Health Science Center, 2025. https://doi.org/10.21007/con.dnp.2025.0098.

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Brzakovic, Dragana. Mammogram Screening by Automated Followup: A Feasibility Study. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada381307.

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Sanchez-Ayendez, Melba M. Mammogram Compliance Among Low-Income Middle-Women in Puerto Rico. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada400641.

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Goldgof, Dmitry. Accurate 3D Modeling of Breast Deformation for Temporal Mammogram Registration. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada495322.

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Li, Lihua. Computer Analysis and Detection of Missed Cancer in Screening Mammogram. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada455785.

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Li, Lihua. Computerized Analysis and Detection of Missed Cancer in Screening Mammogram. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada428194.

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Goldgof, Dmitry. Accurate 3D Modeling of Breast Deformation for Temporal Mammogram Registration. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada516476.

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Li, Lihua. Computerized Analysis and Detection of Missed Cancer in Screening Mammogram. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada474863.

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Sanchez-Ayendez, Melba M. Mammogram Compliance Among Low-Income Middle-Aged Women in Puerto Rico. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada411452.

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Nishikawa, Robert. Computerized Identification of Normal Mammograms. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada448474.

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