Development of a digital duplication system for portable chest radiographs. - Kenneth R. Hoffmann; Kunio Doi; Heber MacMahon; Maryellen L. Giger; Robert M. Nishikawa; Xin-Wei Xu; Lian Yao; Akiko Kano; Michael Carlin (J. Digit. Imaging, 1994)

Optimizing wavelet transform based on supervised learning for detection of microcalcifications in digital mammograms. - Hiroyuki Yoshida; Wei Zhang; Weidong Cai 004; Kunio Doi; Robert M. Nishikawa; Maryellen L. Giger (ICIP (3), 1995)

Reproducibility of an automated scheme for the detection of clustered microcalcifications on digital mammograms. - Robert M. Nishikawa; John Papaioannou; Stephen A. Collins (Image Processing, 1996)

Exploiting context in mammograms: a hierarchical neural network for detecting microcalcifications. - Paul Sajda; Clay D. Spence; John C. Pearson; Robert M. Nishikawa (Image Processing, 1996)

Computer-Aided Diagnosis of Digital Mammography and Ultrasound Images of Breast Mass Lesions. - Maryellen L. Giger; Zhimin Huo; Dulcy E. Wolverton; Carl J. Vyborny; Catherine Moran; Robert A. Schmidt; Hania A. Al-Hallaq; Robert M. Nishikawa; Kunio Doi (Digital Mammography / IWDM, 1998)

Benefits of Computer-Aided Diagnosis (CAD) in Mammographic Diagnosis of Malignant and Benign Clustered Microcalcifications. - Yulei Jiang; Robert M. Nishikawa; Robert A. Schmidt; Charles E. Metz; Maryellen L. Giger; Kunio Doi (Digital Mammography / IWDM, 1998)

Prospective Testing of a Clinical Mammography Workstation for CAD: Analysis of the First 10, 000 Cases. - Robert M. Nishikawa; Maryellen L. Giger; Dulcy E. Wolverton; Robert A. Schmidt; Christopher E. Comstock; John Papaioannou; Stephen A. Collins; Kunio Doi (Digital Mammography / IWDM, 1998)

Requirement of microcalcification detection for computerized classification of malignant and benign clustered microcalcifications. - Yulei Jiang; Robert M. Nishikawa; John Papaioannou (Image Processing, 1998)

Variations in measured performance of CAD schemes due to database composition and scoring protocol. - Robert M. Nishikawa; Laura M. Yarusso (Image Processing, 1998)

Optimization and FROC Analysis of Rule-Based Detection Schemes Using a Multiobjective Approach. - Mark A. Anastasio; Matthew A. Kupinski; Robert M. Nishikawa (IEEE Trans. Medical Imaging, 1998)

Front-end data reduction in computer-aided diagnosis of mammograms: a pilot study. - Hamed Sari-Sarraf; Shaun S. Gleason; Robert M. Nishikawa (Image Processing, 1999)

Hierarchical, Multi-resolution Models for Object Recognition: Applications to Mammographic Computer-aided Diagnosis. - Paul Sajda; Clay Spence; Lucas C. Parra; Robert M. Nishikawa (AIPR, 2000)

An evaluation criterion for edge detection techniques in noisy images. - Francisco L. Valverde; Nicolás Guil; José Muñoz; Robert M. Nishikawa; Kunio Doi (ICIP (1), 2001)

Eliminating false-positive microcalcification clusters in a mammography CAD scheme using a Bayesian neural network. - Darrin C. Edwards; John Papaioannou; Yulei Jiang; Matthew A. Kupinski; Robert M. Nishikawa (Image Processing, 2001)

A support vector machine approach for detection of microcalcifications in mammograms. - Issam El-Naqa; Yongyi Yang; Miles N. Wernick; Nikolas P. Galatsanos; Robert M. Nishikawa (ICIP (2), 2002)

Support vector machine learning for detection of microcalcifications in mammograms. - Issam El-Naqa; Yongyi Yang; Miles N. Wernick; Nikolas P. Galatsanos; Robert M. Nishikawa (ISBI, 2002)

Intelligent CAD workstation for breast imaging using similarity to known lesions and multiple visual prompt aids. - Maryellen Lissak Giger; Zhimin Huo; Carl J. Vyborny; Li Lan; Ioana R. Bonta; Karla Horsch; Robert M. Nishikawa; Ingrid Rosenborough (Image Processing, 2002)

Improving the automated classification of clustered calcifications on mammograms through the improved detection of individual calcifications. - Robert M. Nishikawa; Maria Fernanda Salfity; Yulei Jiang; John Papaioannou (Image Processing, 2002)

A Support Vector Machine Approach for Detection of Microcalcifications. - Issam El-Naqa; Yongyi Yang; Miles N. Wernick; Nikolas P. Galatsanos; Robert M. Nishikawa (IEEE Trans. Medical Imaging, 2002)

Automated selection of BI-RADS lesion descriptors for reporting calcifications in mammograms. - Sophie Paquerault; Yulei Jiang; Robert M. Nishikawa; Robert A. Schmidt; Carl J. D'Orsi; Carl J. Vyborny; Gillian M. Newstead (Image Processing, 2003)

Computerized detection of mammographic masses in digital breast tomosynthesis images using radial gradient index filtering. - Ingrid S. Reiser; Robert M. Nishikawa; Maryellen L. Giger; Elizabeth A. Rafferty; Daniel B. Kopans; Richard H. Moore; Tao Wu (CARS, 2004)

Computer simulation of mammographic imaging for applications in CAD. - Yinghui Zhang; Robert M. Nishikawa (CARS, 2004)

Computerized detection and 3-way classification of breast lesions on ultrasound images. - Karen Drukker; Darrin C. Edwards; Maryellen Lissak Giger; Robert M. Nishikawa; Charles E. Metz (Image Processing, 2004)

A reconstruction-independent method for computerized mass detection in digital tomosynthesis images of the breast. - Ingrid S. Reiser; Emil Y. Sidky; Maryellen Lissak Giger; Robert M. Nishikawa; Elizabeth A. Rafferty; Daniel B. Kopans; Richard H. Moore; Tao Wu (Image Processing, 2004)

A similarity learning approach to content-based image retrieval: application to digital mammography. - Issam El-Naqa; Yongyi Yang; Nikolas P. Galatsanos; Robert M. Nishikawa; Miles N. Wernick (IEEE Trans. Medical Imaging, 2004)

Relevance vector machine learning for detection of microcalcifications in mammograms. - Liyang Wei; Yongyi Yang; Robert M. Nishikawa (ICIP (1), 2005)

A relevance vector machine technique for the automatic detection of clustered microcalcifications (Honorable Mention Poster Award). - Liyang Wei; Yongyi Yang; Robert M. Nishikawa (Image Processing, 2005)

A study of several CAD methods for classification of clustered microcalcifications. - Liyang Wei; Yongyi Yang; Robert M. Nishikawa; Yulei Jiang (Image Processing, 2005)

The hypervolume under the ROC hypersurface of "Near-Guessing" and Near-Perfect" observers in N-class classification tasks. - Darrin C. Edwards; Charles E. Metz; Robert M. Nishikawa (IEEE Trans. Medical Imaging, 2005)

A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications. - Liyang Wei; Yongyi Yang; Robert M. Nishikawa; Yulei Jiang (IEEE Trans. Medical Imaging, 2005)

Relevance vector machine for automatic detection of clustered microcalcifications. - Liyang Wei; Yongyi Yang; Robert M. Nishikawa; Miles N. Wernick; Alexandra Edwards (IEEE Trans. Medical Imaging, 2005)

Mammogram Retrieval by Similarity Learning from Experts. - Liyang Wei; Yongyi Yang; Robert M. Nishikawa; Miles N. Wernick (ICIP, 2006)

Comparison of Computerized Image Analyses for Digitized Screen-Film Mammograms and Full-Field Digital Mammography Images. - Hui Li 024; Maryellen L. Giger; Yading Yuan; Li Lan; Kenji Suzuki 001; Andrew R. Jamieson; Laura Yarusso; Robert M. Nishikawa; Charlene A. Sennett (Digital Mammography / IWDM, 2006)

Modeling the Effect of Computer-Aided Detection on the Sensitivity of Screening Mammography. - Robert M. Nishikawa (Digital Mammography / IWDM, 2006)

Development of an Analytic Breast Phantom for Quantitative Comparison of Reconstruction Algorithms for Digital Breast Tomosynthesis. - Ingrid S. Reiser; Emil Y. Sidky; Robert M. Nishikawa; Xiaochuan Pan (Digital Mammography / IWDM, 2006)

Retrieval-Driven Microcalcification Classification for Breast Cancer Diagnosis. - Liyang Wei; Yongyi Yang; Robert M. Nishikawa (ISBI, 2007)

Current status and future directions of computer-aided diagnosis in mammography. - Robert M. Nishikawa (Comput. Medical Imaging Graph., 2007)

Effect of Scan Angle and Reconstruction Algorithm on Model Observer Performance in Tomosynthesis. - Ingrid S. Reiser; Beverly A. Lau; Robert M. Nishikawa (Digital Mammography / IWDM, 2008)

Influence of signal-to-noise ratio and temporal stability on computer-aided detection of mammographic microcalcifications in digitized screen-film and full-field digital mammography. - Laura M. Yarusso; Robert M. Nishikawa (Computer-Aided Diagnosis, 2008)

Learning of Perceptual Similarity From Expert Readers for Mammogram Retrieval. - Liyang Wei; Yongyi Yang; Miles N. Wernick; Robert M. Nishikawa (IEEE J. Sel. Top. Signal Process., 2009)

Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis. - Liyang Wei; Yongyi Yang; Robert M. Nishikawa (Pattern Recognit., 2009)

An Anthropomorphic Software Breast Phantom for Tomosynthesis Simulation: Power Spectrum Analysis of Phantom Projections. - Predrag R. Bakic; Beverly A. Lau; Ann-Katherine Carton; Ingrid S. Reiser; Andrew D. A. Maidment; Robert M. Nishikawa (Digital Mammography / IWDM, 2010)

Stratified Sampling for Case Selection Criteria for Evaluating CAD. - Robert M. Nishikawa; Lorenzo L. Pesce (Digital Mammography / IWDM, 2010)

Human Observer Performance in a Single Slice or a Volume: Effect of Background Correlation. - Ingrid S. Reiser; Robert M. Nishikawa (Digital Mammography / IWDM, 2010)

Evaluation of a 3D lesion segmentation algorithm on DBT and breast CT images. - 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 (Computer-Aided Diagnosis, 2010)

Textural feature comparison between FFDM and film mammograms. - Hao Jing; Yongyi Yang; Laura Yarusso; Robert M. Nishikawa (ISBI, 2011)

A comparison study of textural features between FFDM and film mammogram images. - Hao Jing; Yongyi Yang; Miles N. Wernick; Laura M. Yarusso; Robert M. Nishikawa (Computer-Aided Diagnosis, 2011)

Estimating Sensitivity and Specificity in an ROC Experiment. - Robert M. Nishikawa (Digital Mammography / IWDM, 2012)

Methods for Evaluating the Effectiveness of Screening Mammography Are Not Necessarily Valid for Evaluating the Effectiveness of Computer-Aided Detection in Screening Mammography. - Robert M. Nishikawa (Digital Mammography / IWDM, 2012)

A Directional Small-Scale Tissue Model for an Anthropomorphic Breast Phantom. - Ingrid S. Reiser; Beverly A. Lau; Robert M. Nishikawa; Predrag R. Bakic (Digital Mammography / IWDM, 2012)

Regularization in Retrieval-Driven Classification of Clustered Microcalcifications for Breast Cancer. - Hao Jing; Yongyi Yang; Robert M. Nishikawa (Int. J. Biomed. Imaging, 2012)

Reduction of false positive detection in clustered microcalcifications. - Juan Wang 007; Yongyi Yang; Robert M. Nishikawa (ICIP, 2013)

Predicting the Benefit of Using CADe in Screening Mammography. - Robert M. Nishikawa; Andriy I. Bandos (Digital Mammography / IWDM, 2014)

Exploring perceptually similar cases with multi-dimensional scaling. - Juan Wang 007; Yongyi Yang; Miles N. Wernick; Robert M. Nishikawa (Computer-Aided Diagnosis, 2014)

Quantitative study of image features of clustered microcalcifications in for-presentation mammograms. - Juan Wang 007; Yongyi Yang; Robert M. Nishikawa (ICIP, 2016)

An image-retrieval aided diagnosis system for clustered microcalcifications. - Juan Wang 007; Yongyi Yang; Miles N. Wernick; Robert M. Nishikawa (ISBI, 2016)

Proposal of Semi-automatic Classification of Breast Lesions for Strain Sonoelastography Using a Dedicated CAD System. - Karem D. Marcomini; Eduardo F. C. Fleury; Homero Schiabel; Robert M. Nishikawa (Digital Mammography / IWDM, 2016)

Agreement Between Radiologists' Interpretations of Screening Mammograms. - Robert M. Nishikawa; Christopher E. Comstock; Michael N. Linver; Gillian M. Newstead; Vinay Sandhir; Robert A. Schmidt (Digital Mammography / IWDM, 2016)

Changes in frequency of recall recommendations of examinations depicting cancer with the availability of either priors or digital breast tomosynthesis. - Christiane M. Hakim; Andriy I. Bandos; Marie A. Ganott; Victor J. Catullo; Denise M. Chough; Amy E. Kelly; Dilip D. Shinde; Jules H. Sumkin; Luisa P. Wallace; Robert M. Nishikawa; David Gur (Image Perception, Observer Performance, and Technology Assessment, 2016)

Can model observers be developed to reproduce radiologists' diagnostic performances? Our study says not so fast! - Juhun Lee; Robert M. Nishikawa; Ingrid S. Reiser; John M. Boone (Image Perception, Observer Performance, and Technology Assessment, 2016)

Neutrosophic segmentation of breast lesions for dedicated breast CT. - Juhun Lee; Robert M. Nishikawa; Ingrid S. Reiser; John M. Boone (Computer-Aided Diagnosis, 2017)

Agreement between a computer-assisted tool and radiologists to classify lesions in breast elastography images. - Karem D. Marcomini; Eduardo F. C. Fleury; Vilmar M. Oliveira; Antonio A. O. Carneiro; Homero Schiabel; Robert M. Nishikawa (Computer-Aided Diagnosis, 2017)

Estimating the Accuracy Level Among Individual Detections in Clustered Microcalcifications. - Maria V. Sainz de Cea; Robert M. Nishikawa; Yongyi Yang (IEEE Trans. Medical Imaging, 2017)

Reducing the effect of false positives in classification of detected clustered microcalcifications. - Maria V. Sainz de Cea; Yongyi Yang; Robert M. Nishikawa (ISBI, 2018)

Developing imaging biomarkers for mammographically-occult cancer in dense breasts using a radiologist's progress rating on cancer development: a preliminary analysis. - Juhun Lee; Sung Eun Song; Robert M. Nishikawa (IWBI, 2018)

Detecting mammographically-occult cancer in women with dense breasts using Radon Cumulative Distribution Transform: a preliminary analysis. - Juhun Lee; Robert M. Nishikawa; Gustavo K. Rohde (Computer-Aided Diagnosis, 2018)

Limiting Level of False-Positive Detections in Classification of Microcalcification Clusters in Mammograms. - María V. Sainz de Cea; Robert M. Nishikawa; Yongyi Yang (ISBI, 2019)

Detecting mammographically-occult cancer in women with dense breasts using deep convolutional neural network and Radon cumulative distribution transform. - Juhun Lee; Robert M. Nishikawa (Computer-Aided Diagnosis, 2019)

Oculomotor behaviour of radiologists reading digital breast tomosynthesis (DBT). - Nicholas M. D'Ardenne; Robert M. Nishikawa; Margarita L. Zuley; Chia-Chien Wu; Jeremy M. Wolfe (Image Perception, Observer Performance, and Technology Assessment, 2019)

Simulating breast mammogram using conditional generative adversarial network: application towards finding mammographically-occult cancer. - Juhun Lee; Robert M. Nishikawa (Computer-Aided Diagnosis, 2020)

Cross-Organ, Cross-Modality Transfer Learning: Feasibility Study for Segmentation and Classification. - Juhun Lee; Robert M. Nishikawa (IEEE Access, 2020)

Estimating near term breast cancer risk from sequential mammograms using deep learning, radon cumulative distribution transform, and a clinical risk factor: preliminary analysis. - Juhun Lee; Robert M. Nishikawa; Andriy Bandos; Margarita L. Zuley (Computer-Aided Diagnosis, 2021)

Improving lesion detection algorithm in digital breast tomosynthesis leveraging ensemble cross-validation models with multi-depth levels. - Belayat Hossain; Robert M. Nishikawa; Juhun Lee (Computer-Aided Diagnosis, 2022)

Analyzing GAN artifacts for simulating mammograms: application towards finding mammographically-occult cancer. - Juhun Lee; Robert M. Nishikawa (Computer-Aided Diagnosis, 2022)

Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated Mammograms. - Juhun Lee; Robert M. Nishikawa (IEEE Trans. Medical Imaging, 2022)

Developing a breast lesion simulator and remover in mammograms using Cycle-GAN: focusing on its impacts on a computer aided detection algorithm. - Juhun Lee; Robert M. Nishikawa (Computer-Aided Diagnosis, 2023)