Deep-LIBRA: Artificial intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment
暂无分享,去创建一个
Emily F. Conant | Aimilia Gastounioti | Despina Kontos | Omid Haji Maghsoudi | Christopher Scott | Stacey Winham | Lauren Pantalone | Fang-Fang Wu | Eric A. Cohen | Celine Vachon | C. Vachon | E. Conant | D. Kontos | Fang-Fang Wu | Christopher Scott | A. Gastounioti | S. Winham | O. H. Maghsoudi | Lauren Pantalone
[1] Pascal Fua,et al. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] Farrukh Nagi,et al. Automated breast profile segmentation for ROI detection using digital mammograms , 2010, 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).
[3] Nico Karssemeijer,et al. Large scale deep learning for computer aided detection of mammographic lesions , 2017, Medical Image Anal..
[4] Ghassan Hamarneh,et al. Geometry-Based Pectoral Muscle Segmentation From MLO Mammogram Views , 2017, IEEE Transactions on Biomedical Engineering.
[5] Samuel J. Magny,et al. Breast Imaging Reporting and Data System , 2020, Definitions.
[6] Sotirios A. Tsaftaris,et al. Medical Image Computing and Computer Assisted Intervention , 2017 .
[7] Keerthi Ram,et al. Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[8] B. Keller,et al. Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation. , 2012, Medical physics.
[9] Jack Cuzick,et al. Long-term Accuracy of Breast Cancer Risk Assessment Combining Classic Risk Factors and Breast Density , 2018, JAMA oncology.
[10] Yahong Luo,et al. A deep learning method for classifying mammographic breast density categories , 2018, Medical physics.
[11] J. Anitha,et al. A dual stage adaptive thresholding (DuSAT) for automatic mass detection in mammograms , 2017, Comput. Methods Programs Biomed..
[12] Kamila Czaplicka,et al. Automatic Breast-Line and Pectoral Muscle Segmentation , 2011 .
[13] Mislav Grgic,et al. Robust automatic breast and pectoral muscle segmentation from scanned mammograms , 2013, Signal Process..
[14] Ralph Highnam,et al. Volumetric Assessment of Breast Tissue Composition from FFDM Images , 2008, Digital Mammography / IWDM.
[15] Suresh Manandhar,et al. Focusnet: An Attention-Based Fully Convolutional Network for Medical Image Segmentation , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).
[16] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[17] Eric A. Cohen,et al. Incorporating Breast Anatomy in Computational Phenotyping of Mammographic Parenchymal Patterns for Breast Cancer Risk Estimation , 2018, Scientific Reports.
[18] Nico Karssemeijer,et al. Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring , 2016, IEEE Transactions on Medical Imaging.
[19] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[20] Berkman Sahiner,et al. Computerized image analysis: estimation of breast density on mammograms , 2000, Medical Imaging: Image Processing.
[21] Andriy Fedorov,et al. Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.
[22] Nico Karssemeijer,et al. Using deep learning to segment breast and fibroglandular tissue in MRI volumes , 2017, Medical physics.
[23] Karla Kerlikowske,et al. Population-Attributable Risk Proportion of Clinical Risk Factors for Breast Cancer , 2017, JAMA oncology.
[24] Reyer Zwiggelaar,et al. Deep learning in mammography and breast histology, an overview and future trends , 2018, Medical Image Anal..
[25] Christos Davatzikos,et al. O-Net: An Overall Convolutional Network for Segmentation Tasks , 2020, MLMI@MICCAI.
[26] Phoebe E. Freer,et al. Mammographic breast density: impact on breast cancer risk and implications for screening. , 2015, Radiographics : a review publication of the Radiological Society of North America, Inc.
[27] Ronilda C. Lacson,et al. Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice: A Multicenter Observational Study. , 2016, Annals of internal medicine.
[28] Lubomir M. Hadjiiski,et al. Automated pectoral muscle identification on MLO-view mammograms: Comparison of deep neural network to conventional computer vision. , 2019, Medical physics.
[29] Yongyi Yang,et al. Pectoral muscle segmentation in mammograms based on homogenous texture and intensity deviation , 2013, Pattern Recognit..
[30] Anne Marie McCarthy,et al. Racial Differences in Quantitative Measures of Area and Volumetric Breast Density. , 2016, Journal of the National Cancer Institute.
[31] E. Conant,et al. Can AI Help Make Screening Mammography "Lean"? , 2019, Radiology.
[32] Nico Karssemeijer,et al. Pectoral muscle segmentation in breast tomosynthesis with deep learning , 2018, Medical Imaging.
[33] E. Krupinski,et al. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. , 2019, Radiology.
[34] N. Breslow,et al. Estimation of multiple relative risk functions in matched case-control studies. , 1978, American journal of epidemiology.
[35] R. Barzilay,et al. Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation. , 2019, Radiology.
[36] C. Vachon,et al. Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction. , 2020, Radiology.
[37] Yianni Attikiouzel,et al. Automatic pectoral muscle segmentation on mediolateral oblique view mammograms , 2004, IEEE Transactions on Medical Imaging.
[38] Hui Wang,et al. A hierarchical pipeline for breast boundary segmentation and calcification detection in mammograms , 2018, Comput. Biol. Medicine.
[39] Woei-Chyn Chu,et al. Performance measure characterization for evaluating neuroimage segmentation algorithms , 2009, NeuroImage.
[40] Hongmin Cai,et al. Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning , 2016, Scientific Reports.
[41] Thomas Frauenfelder,et al. Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer , 2017, Investigative radiology.
[42] R. Barzilay,et al. A Deep Learning Model to Triage Screening Mammograms: A Simulation Study. , 2019, Radiology.
[43] Dag Pavic,et al. Effects of Changes in BI-RADS Density Assessment Guidelines (Fourth Versus Fifth Edition) on Breast Density Assessment: Intra- and Interreader Agreements and Density Distribution. , 2016, AJR. American journal of roentgenology.
[44] Manuela Durando,et al. Radiological assessment of breast density by visual classification (BI–RADS) compared to automated volumetric digital software (Quantra): implications for clinical practice , 2014, La radiologia medica.
[45] Rangaraj M. Rangayyan,et al. Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms , 2015, Medical & Biological Engineering & Computing.
[46] Rangaraj M. Rangayyan,et al. Automatic identification of the pectoral muscle in mammograms , 2004, IEEE Transactions on Medical Imaging.
[47] Karla Kerlikowske,et al. Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening. , 2016, Radiology.
[48] Philip J. Morrow,et al. Fully automated breast boundary and pectoral muscle segmentation in mammograms , 2017, Artif. Intell. Medicine.
[49] Ulas Bagci,et al. Automatically Designing CNN Architectures for Medical Image Segmentation , 2018, MLMI@MICCAI.
[50] Kevin W. Eliceiri,et al. ImageJ2: ImageJ for the next generation of scientific image data , 2017, BMC Bioinformatics.