Maryellen L. Giger

发表

A multiobjective approach to optimizing computerized detection schemes

Robert M. Nishikawa, Matthew A. Kupinski, Mark A. Anastasio, 1998, 1998 IEEE Nuclear Science Symposium Conference Record. 1998 IEEE Nuclear Science Symposium and Medical Imaging Conference (Cat. No.98CH36255).

Towards understanding perception in the latest era of AI in medical imaging (Conference Presentation)

Maryellen L. Giger, 2020, Medical Imaging: Image Perception, Observer Performance, and Technology Assessment.

Feature selection and classifiers for the computerized detection of mass lesions in digital mammography

Matthew A. Kupinski, Maryellen L. Giger, M. Giger, 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

Light‐Based Technologies for a Better World

Maryellen L. Giger, 2017 .

Network output visualization to uncover limitations of deep learning detection of pneumothorax

Heber MacMahon, Feng Li, Maryellen L. Giger, 2020, Medical Imaging.

Deep learning for pneumothorax detection and localization using networks fine-tuned with multiple institutional datasets

Heber MacMahon, Feng Li, Maryellen L. Giger, 2020, Medical Imaging.

Preface to data mining in biomedical informatics and healthcare

Paulo J. G. Lisboa, Jacob D. Furst, Hamidreza Chitsaz, 2013, ICDM 2013.

Development of Digital Processing Te Computer-Aided Diagnosis in Radiographi

Kunio Doi, Kenneth R. Hoffmann, Maryellen L. Giger, 1993 .

Repeatability profiles towards consistent sensitivity and specificity levels for machine learning on breast DCE-MRI

Karen Drukker, Heather M. Whitney, Hiroyuki Abe, 2020, Medical Imaging.

Preliminary radiomic texture analysis of high-channel fluorescence confocal images of triple-negative breast cancer biopsies

Maryellen L. Giger, Madeleine S. Durkee, Rebecca Abraham, 2020, Medical Imaging: Digital Pathology.

Case-based repeatability of machine learning classification performance on breast MRI

Karen Drukker, Heather M. Whitney, Hiroyuki Abe, 2020, Medical Imaging.

Effect of observer variability and training cases on U-Net segmentation performance

David F. Yankelevitz, Claudia I. Henschke, Maryellen L. Giger, 2020, Medical Imaging.

SU‐EE‐A2‐02: Efficient Automatic Pre‐Selection of Mass Lesion Candidates in DBT Breast Volumes

Robert M. Nishikawa, Tao Wu, Ingrid Reiser, 2005 .

Transfer Learning in 4D for Breast Cancer Diagnosis using Dynamic Contrast-Enhanced Magnetic Resonance Imaging pdf

Qiyuan Hu, Heather M. Whitney, Maryellen L. Giger, 2019, ArXiv.

Improvement of classification performance using harmonization across field strength of radiomic features extracted from DCE-MR images of the breast

Heather M. Whitney, Maryellen L. Giger, M. Giger, 2020, Medical Imaging.

Cascade of U-Nets in the detection and classification of coronary artery calcium in thoracic low-dose CT

David F. Yankelevitz, Claudia I. Henschke, Maryellen L. Giger, 2020, Medical Imaging.

Improved instance segmentation of immune cells in human lupus nephritis biopsies with Mask R-CNN

Maryellen L. Giger, Madeleine S. Durkee, Rebecca Abraham, 2020, Medical Imaging: Digital Pathology.

CT Texture Characterization

Maryellen L. Giger, Joseph J. Foy, Samuel G. ArmatoIII, 2020 .

Outlook of the future landscape of artificial intelligence in medicine and new challenges

Maryellen L. Giger, James K. Min, Lei Xing, 2021 .

The automated method and system for medical image segmentation

Ulrich Bick, Maryellen L. Giger, 1994 .

Medical imaging and computers in the diagnosis of breast cancer

Maryellen L. Giger, 2014, Optics & Photonics - Optical Engineering + Applications.

Investigation of regularized neural networks for the computerized detection of mass lesions in digital mammograms

Matthew A. Kupinski, Maryellen L. Giger, 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).