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Maryellen L. Giger
Maryellen Lissak Giger
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2020 – today
- 2024
- [i4]Robert L. Grossman, Ceilyn Boyd, Nhan V. Do, Danne C. Elbers, Michael Sean Fitzsimons, Maryellen L. Giger, Anthony Juehne, Brienna Larrick, Jerry S. H. Lee, Dawei Lin, Michael Lukowski, James D. Myers, Philip Schumm, Aarti Venkat:
Ten Pillars for Data Meshes. CoRR abs/2411.05248 (2024) - 2023
- [c138]Zilinghan Li, Shilan He, Pranshu Chaturvedi, Trung-Hieu Hoang, Minseok Ryu, Eliu A. Huerta, Volodymyr V. Kindratenko, Jordan D. Fuhrman, Maryellen L. Giger, Ryan Chard, Kibaek Kim, Ravi K. Madduri:
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service. e-Science 2023: 1-4 - [c137]J. L. Cozzi, Hui Li, Julian Conn Busch, J. Williams, Li Lan, X. Keutgen, Maryellen L. Giger:
Novel integration of radiomics and deep transfer learning for diagnosis of indeterminate thyroid nodules on ultrasound. Computer-Aided Diagnosis 2023 - [c136]Lin Guo
, Kunlei Hong, Ziqi Zhang, Bin Zheng, Stefan Jaeger, Jordan D. Fuhrman, Hui Li, Maryellen L. Giger, Andrei Gabrielian, Alex Rosenthal, Darrell E. Hurt, Ziv Yaniv, Y. M. Fleming Lure:
Assessing an AI-based smart imagery framing and truthing (SIFT) system to assist radiologists annotating lung abnormalities on chest x-ray images for development of deep learning models. Computer-Aided Diagnosis 2023 - [c135]Mena Shenouda, Aditi Kaveti, Isabella Flerlage, Jayashree Kalpathy-Cramer, Maryellen L. Giger, Samuel G. Armato III:
Assessing robustness of a deep-learning model for COVID-19 classification on chest radiographs. Computer-Aided Diagnosis 2023 - [c134]Lindsay Douglas, Trisha Mondal
, Alexandra Edwards, Maryellen L. Giger:
Influence of magnet strength on background parenchymal enhancement evaluation. Image Perception, Observer Performance, and Technology Assessment 2023 - [c133]Karen Drukker
, Hui Li, Maryellen L. Giger:
Longitudinal robustness of a thoracic radiographic AI model for COVID-19 severity prediction. Image Perception, Observer Performance, and Technology Assessment 2023 - [c132]Karen Drukker
, Berkman Sahiner, Tingting Hu, Hyun J. Grace Kim, Heather M. Whitney, Natalie M. Baughan, Kyle J. Myers, Maryellen L. Giger, Michael F. McNitt-Gray:
Assistance tools for the evaluation of machine learning algorithm performance: the decision tree-based tools developed by the Medical Imaging and Data Resource Center (MIDRC) Technology Development Project (TDP) 3c effort. Image Perception, Observer Performance, and Technology Assessment 2023 - [c131]Madeleine S. Durkee, Nevaeh Petrie, Kyle Lleras, Junting Ai, Rebecca Abraham, J. Cy Chittenden, Chasity Kasir, Fiona Clark, Gabriel Casella, Marcus R. Clark, Maryellen L. Giger:
Convolutional neural networks detect cells in densely packed images at performance levels similar to human readers. Image Perception, Observer Performance, and Technology Assessment 2023 - [c130]Heather M. Whitney, Hui Li, Karen Drukker
, Michael Reeve, Maryellen L. Giger:
Investigation of demographic implicit discrimination and disparate impact in chest radiography image-based AI for COVID-19 severity prediction. Image Perception, Observer Performance, and Technology Assessment 2023 - [i3]Zilinghan Li, Shilan He, Pranshu Chaturvedi, Trung-Hieu Hoang, Minseok Ryu, Eliu A. Huerta, Volodymyr V. Kindratenko, Jordan D. Fuhrman, Maryellen L. Giger, Ryan Chard, Kibaek Kim, Ravi K. Madduri
:
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service. CoRR abs/2308.08786 (2023) - [i2]Trung-Hieu Hoang, Jordan D. Fuhrman, Ravi K. Madduri, Miao Li, Pranshu Chaturvedi, Zilinghan Li, Kibaek Kim, Minseok Ryu, Ryan Chard, Eliu A. Huerta, Maryellen L. Giger:
Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx. CoRR abs/2312.08701 (2023) - 2022
- [c129]Natalie M. Baughan, Lindsay Douglas
, Maya Ballard, Esther Seoyeon Lee, Alexandra Edwards, Li Lan, Hui Li, Maryellen L. Giger:
Association between DCE MRI background parenchymal enhancement and mammographic texture features. Computer-Aided Diagnosis 2022 - [c128]Heather M. Whitney, Yu Ji, Hui Li, Peifang Liu, Maryellen L. Giger:
Effect of different molecular subtype reference standards in AI training: implications for DCE-MRI radiomics of breast cancers. Computer-Aided Diagnosis 2022 - [c127]Natalie M. Baughan, Heather M. Whitney, Karen Drukker
, Berkman Sahiner, Tingting Hu, Hyun J. Grace Kim, Michael F. McNitt-Gray, Kyle J. Myers, Maryellen L. Giger:
Sequestration of imaging studies in MIDRC: a multi-institutional data commons. Image Perception, Observer Performance, and Technology Assessment 2022 - [c126]Heather M. Whitney, Karen Drukker
, Hiroyuki Abe, Maryellen L. Giger:
Case-based repeatability and operating point variability of AI: breast lesion classification based on deep transfer learning. Image Perception, Observer Performance, and Technology Assessment 2022 - 2021
- [j20]Feng Li
, Samuel G. Armato, Roger Engelmann, Thomas Rhines, Jennie Crosby, Li Lan, Maryellen L. Giger, Heber MacMahon:
Anatomic Point-Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs. J. Digit. Imaging 34(4): 922-931 (2021) - [c125]Paul Amstutz, Karen Drukker
, Hui Li, Hiroyuki Abe, Maryellen L. Giger, Heather M. Whitney:
Case-based diagnostic classification repeatability using radiomic features extracted from full-field digital mammography images of breast lesions. Computer-Aided Diagnosis 2021 - [c124]Natalie M. Baughan, Hui Li, Li Lan, Chun-Wai Chan, Matthew Embury, Gary Whitman, Randa El-Zein, Isabelle Bedrosian, Maryellen L. Giger:
Parenchymal field effect analysis for breast cancer risk assessment: evaluation of FFDM radiomic similarity. Computer-Aided Diagnosis 2021 - [c123]Roma Bhattacharjee, Lindsay Douglas
, Karen Drukker
, Qiyuan Hu
, Jordan D. Fuhrman, Deepa Sheth, Maryellen L. Giger:
Comparison of 2D and 3D U-Net breast lesion segmentations on DCE-MRI. Computer-Aided Diagnosis 2021 - [c122]Lindsay Douglas
, Deepa Sheth, Maryellen L. Giger:
Electronic removal of lesions for more robust BPE scoring on breast DCE-MRI. Computer-Aided Diagnosis 2021 - [c121]Jordan D. Fuhrman, Linnea Kremer, Yeqing Zhu
, Rowena Yip, Feng Li, Li Lan, Hui Li, Artit C. Jirapatnakul, Claudia I. Henschke, David F. Yankelevitz, Maryellen L. Giger:
Radiomic texture analysis for the assessment of osteoporosis on low-dose thoracic CT scans. Computer-Aided Diagnosis 2021 - [c120]Qiyuan Hu
, Karen Drukker
, Maryellen L. Giger:
Role of standard and soft tissue chest radiography images in COVID-19 diagnosis using deep learning. Computer-Aided Diagnosis 2021 - [c119]Rebecca Abraham, Madeleine S. Durkee, Margaret Veselits, Junting Ai, Jordan D. Fuhrman, Marcus R. Clark, Maryellen L. Giger:
Application and generalizability of U-Net segmentation of immune cells in inflamed tissue. Digital Pathology 2021 - [c118]Madeleine S. Durkee, Rebecca Abraham, Junting Ai, Marcus R. Clark, Maryellen L. Giger:
Managing class imbalance and differential staining of immune cell populations in multi-class instance segmentation of multiplexed immunofluorescence images of Lupus Nephritis biopsies. Digital Pathology 2021 - [c117]Bradie M. Ferguson, Madeleine S. Durkee, Rebecca Abraham, Junting Ai, Hui Li, Li Lan, Julian Bertini, Marcus R. Clark, Maryellen L. Giger:
Radiomic texture analysis of immunofluorescence images of lupus nephritis biopsies to predict patient progression to end-stage renal disease. Digital Pathology 2021 - [c116]Michelle de Oliveira, Karen Drukker
, Michael Vieceli, Hiroyuki Abe, Maryellen L. Giger, Heather M. Whitney:
Comparison of diagnostic performances, case-based repeatability, and operating sensitivity and specificity in classification of breast lesions using DCE-MRI. Image Perception, Observer Performance, and Technology Assessment 2021 - 2020
- [j19]Heather M. Whitney
, Hui Li
, Yu Ji, Peifang Liu, Maryellen L. Giger
:
Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Method. Proc. IEEE 108(1): 163-177 (2020) - [c115]Jennie Crosby, Thomas Rhines, Feng Li, Heber MacMahon, Maryellen L. Giger:
Deep learning for pneumothorax detection and localization using networks fine-tuned with multiple institutional datasets. Computer-Aided Diagnosis 2020 - [c114]Karen Drukker
, Alexandra Edwards, John Papaioannou, Maryellen L. Giger:
Deep learning predicts breast cancer recurrence in analysis of consecutive MRIs acquired during the course of neoadjuvant chemotherapy. Computer-Aided Diagnosis 2020 - [c113]Jordan D. Fuhrman, Rowena Yip, Artit C. Jirapatnakul
, Claudia I. Henschke, David F. Yankelevitz, Maryellen L. Giger:
Cascade of U-Nets in the detection and classification of coronary artery calcium in thoracic low-dose CT. Computer-Aided Diagnosis 2020 - [c112]Qiyuan Hu
, Heather M. Whitney, Maryellen L. Giger:
Using ResNet feature extraction in computer-aided diagnosis of breast cancer on 927 lesions imaged with multiparametric MRI. Computer-Aided Diagnosis 2020 - [c111]Michael Vieceli, Amy Van Dusen, Karen Drukker
, Hiroyuki Abe, Maryellen L. Giger, Heather M. Whitney:
Case-based repeatability of machine learning classification performance on breast MRI. Computer-Aided Diagnosis 2020 - [c110]Heather M. Whitney, Maryellen L. Giger:
Improvement of classification performance using harmonization across field strength of radiomic features extracted from DCE-MR images of the breast. Computer-Aided Diagnosis 2020 - [c109]Madeleine S. Durkee
, Bradie M. Ferguson, Rebecca Abraham, Li Lan, Hui Li, Marcus R. Clark, Maryellen L. Giger:
Preliminary radiomic texture analysis of high-channel fluorescence confocal images of triple-negative breast cancer biopsies. Digital Pathology 2020: 1132013 - [c108]Madeleine S. Durkee
, Adam R. Sibley, Junting Ai, Rebecca Abraham, Vladimir M. Liarski
, Marcus R. Clark, Maryellen L. Giger:
Improved instance segmentation of immune cells in human lupus nephritis biopsies with Mask R-CNN. Digital Pathology 2020: 1132019 - [c107]Jennie Crosby, Sophia Chen, Feng Li, Heber MacMahon, Maryellen L. Giger:
Network output visualization to uncover limitations of deep learning detection of pneumothorax. Image Perception, Observer Performance, and Technology Assessment 2020: 113160O - [c106]Amy Van Dusen, Michael Vieceli, Karen Drukker
, Hiroyuki Abe, Maryellen L. Giger, Heather M. Whitney:
Repeatability profiles towards consistent sensitivity and specificity levels for machine learning on breast DCE-MRI. Image Perception, Observer Performance, and Technology Assessment 2020: 113160I - [c105]Jordan D. Fuhrman, Peter Halloran, Rowena Yip, Artit C. Jirapatnakul
, Claudia I. Henschke, David F. Yankelevitz, Maryellen L. Giger:
Effect of observer variability and training cases on U-Net segmentation performance. Image Perception, Observer Performance, and Technology Assessment 2020: 113160T - [c104]Maryellen L. Giger:
Towards understanding perception in the latest era of AI in medical imaging (Conference Presentation). Image Perception, Observer Performance, and Technology Assessment 2020: 1131602
2010 – 2019
- 2019
- [c103]Heather M. Whitney, Yu Ji, Hui Li, Alexandra Edwards, John Papaioannou, Peifang Liu, Maryellen L. Giger:
Effect of diversity of patient population and acquisition systems on the use of radiomics and machine learning for classification of 2, 397 breast lesions. Computer-Aided Diagnosis 2019: 109501A - [c102]Karen Drukker
, Iman El-Bawab, Alexandra Edwards, Christopher Doyle, John Papaioannou, Kirti Kulkarni, Maryellen L. Giger:
Breast MRI radiomics for the pre-treatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients. Computer-Aided Diagnosis 2019: 109502N - [c101]Qiyuan Hu
, Heather M. Whitney, Alexandra Edwards, John Papaioannou, Maryellen L. Giger:
Radiomics and deep learning of diffusion-weighted MRI in the diagnosis of breast cancer. Computer-Aided Diagnosis 2019: 109504A - [c100]Rachel Anderson, Hui Li, Yu Ji, Peifang Liu, Maryellen L. Giger:
Evaluating deep learning techniques for dynamic contrast-enhanced MRI in the diagnosis of breast cancer. Computer-Aided Diagnosis 2019: 1095006 - [c99]Jordan D. Fuhrman, Jennie Crosby
, Rowena Yip, Claudia I. Henschke, David F. Yankelevitz, Maryellen L. Giger:
Detection and classification of coronary artery calcifications in low dose thoracic CT using deep learning. Computer-Aided Diagnosis 2019: 1095039 - [c98]Kayla R. Mendel, Hui Li, Nabihah Tayob, Randa El-Zein, Isabelle Bedrosian, Maryellen L. Giger:
Temporal mammographic registration for evaluation of architecture changes in cancer risk assessment. Computer-Aided Diagnosis 2019: 1095041 - [i1]Qiyuan Hu, Heather M. Whitney, Maryellen L. Giger:
Transfer Learning in 4D for Breast Cancer Diagnosis using Dynamic Contrast-Enhanced Magnetic Resonance Imaging. CoRR abs/1911.03022 (2019) - 2018
- [c97]Heather M. Whitney, Karen Drukker
, Alexandra Edwards, John Papaioannou, Maryellen L. Giger:
Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers. IWBI 2018: 107180H - [c96]Hui Li, Deepa Sheth, Kayla R. Mendel, Li Lan, Maryellen L. Giger:
Deep learning in computer-aided diagnosis incorporating mammographic characteristics of both tumor and parenchyma stroma. IWBI 2018: 1071806 - [c95]Taylor A. Hinsdale, Bilal H. Malik, Shuna Cheng, Maryellen L. Giger, John Wright, Paras Patel, Javier A. Jo, Kristen C. Maitland
:
Optical detection of oral carcinoma via structured illumination fluorescence lifetime imaging. Biomedical Applications in Molecular, Structural, and Functional Imaging 2018: 105780X - [c94]Hui Li, Kayla R. Mendel, John H. Lee, Li Lan, Maryellen L. Giger:
Deep learning in breast cancer risk assessment: evaluation of fine-tuned convolutional neural networks on a clinical dataset of FFDMs. Computer-Aided Diagnosis 2018: 105750S - [c93]Kayla R. Mendel, Hui Li, Deepa Sheth, Maryellen L. Giger:
Transfer learning with convolutional neural networks for lesion classification on clinical breast tomosynthesis. Computer-Aided Diagnosis 2018: 105750T - [c92]Heather M. Whitney
, Karen Drukker
, Alexandra Edwards, John Papaioannou, Maryellen L. Giger:
Robustness of radiomic breast features of benign lesions and luminal A cancers across MR magnet strengths. Computer-Aided Diagnosis 2018: 105750A - [c91]Joseph J. Foy, Prerana Mitta, Lauren R. Nowosatka, Kayla R. Mendel, Hui Li, Maryellen L. Giger, Hania A. Al-Hallaq
, Samuel G. Armato III:
Variations in algorithm implementation among quantitative texture analysis software packages. Computer-Aided Diagnosis 2018: 105751K - [c90]Natasha Antropova, Benjamin Q. Huynh, Maryellen L. Giger:
Recurrent neural networks for breast lesion classification based on DCE-MRIs. Computer-Aided Diagnosis 2018: 105752M - [c89]Karen Drukker
, Rachel Anderson, Alexandra Edwards, John Papaioannou, Fred Pineda, Hiroyuki Abe, Gregory Karzcmar, Maryellen L. Giger:
Radiomics for ultrafast dynamic contrast-enhanced breast MRI in the diagnosis of breast cancer: a pilot study. Computer-Aided Diagnosis 2018: 105753U - [c88]Kayla R. Mendel, Hui Li, Li Lan, Chun-Wai Chan, Lauren M. King, Nabihah Tayob, Gary Whitman, Randa El-Zein, Isabelle Bedrosian, Maryellen L. Giger:
Temporal assessment of radiomic features on clinical mammography in a high-risk population. Computer-Aided Diagnosis 2018: 105753Q - [c87]Adam R. Sibley, Maryellen L. Giger, Vladimir Liarski
, Marcus R. Clark:
Simultaneous segmentation and classification of multichannel immuno-fluorescently labeled confocal microscopy images using deep convolutional neural networks. Digital Pathology 2018: 1058110 - 2017
- [c86]Benjamin Q. Huynh
, Natasha Antropova, Maryellen L. Giger:
Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning. Computer-Aided Diagnosis 2017: 101340U - [c85]Natasha Antropova, Benjamin Q. Huynh, Maryellen L. Giger:
Performance comparison of deep learning and segmentation-based radiomic methods in the task of distinguishing benign and malignant breast lesions on DCE-MRI. Computer-Aided Diagnosis 2017: 101341G - [c84]Karen Drukker
, Benjamin Q. Huynh, Maryellen L. Giger, Serghei Malkov, Jesús I. Ávila, Bo Fan, Bonnie N. Joe, Karla Kerlikowske, Jennifer S. Drukteinis, Leila Kazemi, Malesa M. Pereira
, John Shepherd
:
Deep learning and three-compartment breast imaging in breast cancer diagnosis. Computer-Aided Diagnosis 2017: 101341F - 2016
- [c83]Serghei Malkov
, Jesús Ávila, Bo Fan, Bonnie N. Joe, Karla Kerlikowske, Maryellen L. Giger, Karen Drukker
, Jennifer S. Drukteinis, Leila Kazemi, Malesa Pereira
, John Shepherd
:
Calibration Procedure of Three Component Mammographic Breast Imaging. Digital Mammography / IWDM 2016: 211-218 - [c82]Jesús Ávila, Serghei Malkov
, Maryellen L. Giger, Karen Drukker
, John A. Shepherd
:
Energy Dependence of Water and Lipid Calibration Materials for Three-Compartment Breast Imaging. Digital Mammography / IWDM 2016: 554-563 - [c81]Adam R. Sibley, Erica Markiewicz, Devkumar Mustafi, Xiaobing Fan, Suzanne D. Conzen, Gregory S. Karczmar, Maryellen L. Giger:
Computerized segmentation algorithm with personalized atlases of murine MRIs in a SV40 large T-antigen mouse mammary cancer model. Biomedical Applications in Molecular, Structural, and Functional Imaging 2016: 97882M - [c80]Karen Drukker
, Serghei Malkov
, Jesús Ávila, Karla Kerlikowske, Bonnie N. Joe, Gregor Krings, Jennifer Creasman, Jennifer S. Drukteinis, Malesa M. Pereira
, Leila Kazemi, John Shepherd
, Maryellen L. Giger:
Identification, segmentation, and characterization of microcalcifications on mammography. Computer-Aided Diagnosis 2016: 97850S - [c79]Kayla R. Mendel, Hui Li, Maryellen L. Giger:
Quantitative breast MRI radiomics for cancer risk assessment and the monitoring of high-risk populations. Computer-Aided Diagnosis 2016: 97851W - 2014
- [j18]Hsien-Chi Kuo, Maryellen L. Giger
, Ingrid S. Reiser, John M. Boone, Karen K. Lindfors, Kai Yang, Alexandra Edwards:
Level Set Segmentation of Breast Masses in Contrast-Enhanced Dedicated Breast CT and Evaluation of Stopping Criteria. J. Digit. Imaging 27(2): 237-247 (2014) - [c78]Serghei Malkov
, Fred Duewer, Karla Kerlikowske, Karen Drukker
, Maryellen L. Giger
, John Shepherd
:
Compositional Three-Component Breast Imaging of Fibroadenoma and Invasive Cancer Lesions: Pilot Study. Digital Mammography / IWDM 2014: 109-114 - [c77]Maryellen L. Giger
, Hui Li, Li Lan, Hiroyuki Abe, Gillian Newstead:
Quantitative MRI Phenotyping of Breast Cancer across Molecular Classification Subtypes. Digital Mammography / IWDM 2014: 195-200 - [c76]Karen Drukker
, Maryellen L. Giger
, Fred Duewer, Serghei Malkov
, Christopher I. Flowers, Bonnie N. Joe, Karla Kerlikowske, Jennifer S. Drukteinis, John Shepherd
:
Roles of biologic breast tissue composition and quantitative image analysis of mammographic images in breast tumor characterization. Computer-Aided Diagnosis 2014: 90351U - 2013
- [j17]Karen Drukker
, Maryellen L. Giger
, Lina Arbash Meinel, Adam Starkey, Jyothi Janardanan, Hiroyuki Abe:
Quantitative ultrasound image analysis of axillary lymph node status in breast cancer patients. Int. J. Comput. Assist. Radiol. Surg. 8(6): 895-903 (2013) - [c75]Yahui Peng, Yulei Jiang, Tatjana Antic, Maryellen L. Giger
, Scott Eggener, Aytekin Oto:
A study of T2-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer. Computer-Aided Diagnosis 2013: 86701H - [c74]Hsien-Chi Kuo, Maryellen L. Giger
, Ingrid S. Reiser, Karen Drukker
, Alexandra Edwards, Charlene A. Sennett:
Automatic 3D lesion segmentation on breast ultrasound images. Computer-Aided Diagnosis 2013: 867025 - 2012
- [j16]Hui Li
, Maryellen L. Giger
, Li Lan, Jeremy Bancroft Brown
, Aoife MacMahon, Mary Mussman, Olufunmilayo I. Olopade, Charlene A. Sennett:
Computerized Analysis of Mammographic Parenchymal Patterns on a Large Clinical Dataset of Full-Field Digital Mammograms: Robustness Study with Two High-Risk Datasets. J. Digit. Imaging 25(5): 591-598 (2012) - [c73]Hsien-Chi Kuo, Maryellen L. Giger
, Ingrid S. Reiser, John M. Boone, Karen K. Lindfors, Kai Yang, Alexandra Edwards:
Level Set Breast Mass Segmentation in Contrast-Enhanced and Non-Contrast-Enhanced Breast CT. Digital Mammography / IWDM 2012: 697-704 - [c72]H. Kuo, Maryellen L. Giger
, Ingrid S. Reiser, John M. Boone, Karen K. Lindfors, K. Yang, Alexandra Edwards:
Evaluation of stopping criteria for level set segmentation of breast masses in contrast-enhanced dedicated breast CT. Computer-Aided Diagnosis 2012: 83152C - [c71]Yahui Peng, Yulei Jiang, Vladimir M. Liarski
, Natalya Kaverina, Marcus R. Clark, Maryellen L. Giger
:
Computerized image analysis of cell-cell interactions in human renal tissue by using multi-channel immunoflourescent confocal microscopy. Computer-Aided Diagnosis 2012: 83153F - [c70]Andrew R. Jamieson
, Karen Drukker
, Maryellen L. Giger
:
Breast image feature learning with adaptive deconvolutional networks. Computer-Aided Diagnosis 2012: 831506 - 2011
- [c69]Andrew R. Jamieson
, Rabi Alam, Maryellen L. Giger
:
Exploring deep parametric embeddings for breast CADx. Computer-Aided Diagnosis 2011: 79630Y - [c68]Neha Bhooshan, Darrin C. Edwards, Maryellen L. Giger
:
Comparison of two-class and three-class Bayesian artificial neural networks in estimation of observations drawn from simulated bivariate normal distributions. Computer-Aided Diagnosis 2011: 796325 - 2010
- [j15]Weijie Chen
, Charles E. Metz, Maryellen L. Giger
, Karen Drukker
:
A Novel Hybrid Linear/Nonlinear Classifier for Two-Class Classification: Theory, Algorithm, and Applications. IEEE Trans. Medical Imaging 29(2): 428-441 (2010) - [c67]Neha Bhooshan, Maryellen L. Giger
, Karen Drukker
, Yading Yuan, Hui Li, Stephanie McCann, Gillian Newstead, Charlene A. Sennett:
Performance of Triple-Modality CADx on Breast Cancer Diagnostic Classification. Digital Mammography / IWDM 2010: 9-14 - [c66]Hui Li, Maryellen L. Giger
, Olufunmilayo I. Olopade, Li Lan:
Validation of Mammographic Texture Analysis for Assessment of Breast Cancer Risk. Digital Mammography / IWDM 2010: 267-271 - [c65]Jeremy Bancroft Brown
, Maryellen L. Giger, Neha Bhooshan, Gillian Newstead, Sanaz Jansen:
Optimization of a fuzzy C-means approach to determining probability of lesion malignancy and quantifying lesion enhancement heterogeneity in breast DCE-MRI. Computer-Aided Diagnosis 2010: 76241I - [c64]Karen Drukker
, Lorenzo L. Pesce
, Maryellen L. Giger:
Repeatability and classifier bias in computer-aided diagnosis for breast ultrasound. Computer-Aided Diagnosis 2010: 76242B - [c63]Hui Li, Maryellen L. Giger, Li Lan, Yading Yuan, Neha Bhooshan, Olufunmilayo I. Olopade:
Effect of variable gain on computerized texture analysis on digitalized mammograms. Computer-Aided Diagnosis 2010: 76242C - [c62]Ingrid S. Reiser, S. P. Joseph, Robert M. Nishikawa
, Maryellen L. Giger, John M. Boone, Karen K. Lindfors, Alexandra Edwards, Nathan J. Packard, Richard H. Moore, Daniel B. Kopans:
Evaluation of a 3D lesion segmentation algorithm on DBT and breast CT images. Computer-Aided Diagnosis 2010: 76242N
2000 – 2009
- 2009
- [j14]Karen Drukker
, Charlene A. Sennett, Maryellen L. Giger
:
Automated Method for Improving System Performance of Computer-Aided Diagnosis in Breast Ultrasound. IEEE Trans. Medical Imaging 28(1): 122-128 (2009) - [c61]Neha Bhooshan, Maryellen L. Giger, Darrin C. Edwards, Karen Drukker, Sanaz Jansen, Hui Li, Li Lan, Gillian Newstead:
Using three-class BANN classifier in the automated analysis of breast cancer lesions in DCE-MRI. Computer-Aided Diagnosis 2009: 72600J - [c60]Hui Li, Maryellen L. Giger
, Yading Yuan, Sanaz A. Jansen, Li Lan, Neha Bhooshan, Gillian M. Newstead:
Computerized breast parenchymal analysis on DCE-MRI. Computer-Aided Diagnosis 2009: 72600N - [c59]Yading Yuan, Maryellen L. Giger
, Hui Li, Charlene A. Sennett:
Breast cancer classification with mammography and DCE-MRI. Computer-Aided Diagnosis 2009: 72600O - [c58]Karen Drukker
, Nicholas Gruszauskas, Maryellen L. Giger
:
Principal component analysis, classifier complexity, and robustness of sonographic breast lesion classification. Computer-Aided Diagnosis 2009: 72602B - [e3]Nico Karssemeijer, Maryellen L. Giger:
Medical Imaging 2009: Computer-Aided Diagnosis, Lake Buena Vista (Orlando Area), Florida, United States, 7-12 February 2009. SPIE Proceedings 7260, SPIE 2009, ISBN 9780819475114 [contents] - 2008
- [j13]Hui Li
, Maryellen L. Giger
, Olufunmilayo I. Olopade, Michael R. Chinander:
Power Spectral Analysis of Mammographic Parenchymal Patterns for Breast Cancer Risk Assessment. J. Digit. Imaging 21(2): 145-152 (2008) - [c57]Yading Yuan, Maryellen L. Giger
, Hui Li, Li Lan, Charlene A. Sennett:
Identifying Corresponding Lesions from CC and MLO Views Via Correlative Feature Analysis. Digital Mammography / IWDM 2008: 323-328 - [c56]Hui Li, Maryellen L. Giger
, Yading Yuan, Li Lan, Charlene A. Sennett:
Performance of CADx on a Large Clinical Database of FFDM Images. Digital Mammography / IWDM 2008: 510-514 - [c55]Karen Drukker
, Maryellen L. Giger
:
Computerized self-assessment of automated lesion segmentation in breast ultrasound: implication for CADx applied to findings in the axilla. Computer-Aided Diagnosis 2008: 69150G - [c54]Zachary B. Rodgers, Martin King, Maryellen L. Giger
, Michael W. Vannier
, Dianna M. E. Bardo, Kenji Suzuki
, Li Lan:
Computerized assessment of coronary calcified plaques in CT images of a dynamic cardiac phantom. Computer-Aided Diagnosis 2008: 69150M - [c53]Yading Yuan, Maryellen L. Giger
, Hui Li, Charlene A. Sennett:
Correlative feature analysis of FFDM images. Computer-Aided Diagnosis 2008: 69151L - [c52]Weijie Chen, Charles E. Metz, Maryellen L. Giger
:
Hybrid linear classifier for jointly normal data: theory. Computer-Aided Diagnosis 2008: 691504 - [e2]Maryellen L. Giger, Nico Karssemeijer:
Medical Imaging 2008: Computer-Aided Diagnosis, San Diego, California, United States, 16-21 February 2008. SPIE Proceedings 6915, SPIE 2008, ISBN 9780819470997 [contents] - 2007
- [c51]Maryellen L. Giger
, Yading Yuan, Hui Li, Karen Drukker
, Weijie Chen, Li Lan, Karla Horsch:
Progress in Breast Cadx. ISBI 2007: 508-511 - [c50]Weijie Chen, Richard M. Zur, Maryellen L. Giger
:
Joint feature selection and classification using a Bayesian neural network with automatic relevance determination priors: potential use in CAD of medical imaging. Computer-Aided Diagnosis 2007: 65141G - [c49]Karen Drukker
, Charlene A. Sennett, Maryellen L. Giger
:
The effect of image quality on the appearance of lesions on breast ultrasound: implications for CADx. Computer-Aided Diagnosis 2007: 65141E - [c48]