John Papaioannou

发表

Radiomics and deep learning of diffusion-weighted MRI in the diagnosis of breast cancer

Maryellen L. Giger, Qiyuan Hu, Heather M. Whitney, 2019, Medical Imaging.

Deep learning predicts breast cancer recurrence in analysis of consecutive MRIs acquired during the course of neoadjuvant chemotherapy

Maryellen L. Giger, Karen Drukker, Alexandra Edwards, 2020, Medical Imaging.

Potential improvement of computerized classification for malignant versus benign mammographic microcalcification clusters with the use of special views

John Papaioannou, Sophie Paquerault, Laura M. Yarusso, 2004, SPIE Medical Imaging.

Computer-aided detection and diagnosis of masses and clustered microcalcifications from digital mammograms

Ping Lu, Robert M. Nishikawa, Maryellen L. Giger, 1993, Electronic Imaging.

Observers' ability to judge the similarity of clustered calcifications on mammograms

Robert M. Nishikawa, Yongyi Yang, John Papaioannou, 2004, SPIE Medical Imaging.

Requirement of microcalcification detection for computerized classification of malignant and benign clustered microcalcifications

Robert M. Nishikawa, John Papaioannou, Yulei Jiang, 1998, Medical Imaging.

Improved computerized detection of individual microcalcifications to integrate cluster detection and classification schemes

Robert M. Nishikawa, John Papaioannou, Yulei Jiang, 2003 .

Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers

Maryellen L. Giger, Karen Drukker, Heather M. Whitney, 2019, Journal of medical imaging.

Radiologists' ability to discriminate computer-detected true and false positives from an automated scheme for the detection of clustered microcalcifications on digital mammograms

Robert M. Nishikawa, John Papaioannou, Robert A. Schmidt, 1997, Medical Imaging.

An integrated CT-imaging, CAD-based system for orthopedic surgery

Kao-Shing Hwang, John Papaioannou, S. David Stulberg, 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

Prospective Testing of a Clinical Mammography Workstation for CAD: Analysis of the First 10, 000 Cases

Robert M. Nishikawa, Maryellen L. Giger, Kunio Doi, 1998, Digital Mammography / IWDM.

Breast MRI radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients

Maryellen L. Giger, Karen Drukker, Alexandra Edwards, 2019, Journal of medical imaging.

Eliminating false-positive microcalcification clusters in a mammography CAD scheme using a Bayesian neural network

Robert M. Nishikawa, Matthew A. Kupinski, John Papaioannou, 2001, SPIE Medical Imaging.

Effect of radiologists' variability on the performance of computer classification of malignant and benign clustered microcalcifications in mammograms

Robert M. Nishikawa, John Papaioannou, Sophie Paquerault, 2003, SPIE Medical Imaging.

The Localisation Outsourcing Decision: How to

John Papaioannou, J. Papaioannou, 2005 .

Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers

Maryellen L. Giger, Karen Drukker, Heather M. Whitney, 2018, Other Conferences.

Effect of diversity of patient population and acquisition systems on the use of radiomics and machine learning for classification of 2,397 breast lesions

Maryellen L. Giger, Heather M. Whitney, Alexandra Edwards, 2019, Medical Imaging.

Initial experience with a prototype clinical intelligent mammography workstation for computer-aided diagnosis

Ping Lu, Robert M. Nishikawa, Maryellen L. Giger, 1995, Medical Imaging.

Learning the Greek Language via Greeklish

Alexandros Karakos, John Papaioannou, Anastasia Georgiadou, 2012, IxD&A.

Improving the automated classification of clustered calcifications on mammograms through the improved detection of individual calcifications

Robert M. Nishikawa, John Papaioannou, Yulei Jiang, 2002, SPIE Medical Imaging.

Robustness of radiomic breast features of benign lesions and luminal A cancers across MR magnet strengths

Maryellen L. Giger, Karen Drukker, Heather M. Whitney, 2018, Medical Imaging.

Reproducibility of an automated scheme for the detection of clustered microcalcifications on digital mammograms

Robert M. Nishikawa, John Papaioannou, Stephen A. Collins, 1996, Medical Imaging.

Breast MRI radiomics for the pre-treatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients

Maryellen L. Giger, Karen Drukker, Alexandra Edwards, 2019, Medical Imaging.

Radiomics for ultrafast dynamic contrast-enhanced breast MRI in the diagnosis of breast cancer: a pilot study

Maryellen L. Giger, Karen Drukker, Hiroyuki Abe, 2018, Medical Imaging.

Comparison of rule-based and artificial neural network approaches for improving the automated detection of clustered microcalcifications in mammograms

Robert M. Nishikawa, Maryellen L. Giger, Kunio Doi, 1995, Optical Engineering Midwest.

Preliminary evaluation of an

Robert M. Nishikawa, Maryellen L. Giger, John Papaioannou, 1993 .

Can computer-aided diagnosis (CAD) help radiologists find mammographically missed screening cancers?

Robert M. Nishikawa, Maryellen L. Giger, John Papaioannou, 2001, SPIE Medical Imaging.

Additive Benefit of Radiomics Over Size Alone in the Distinction Between Benign Lesions and Luminal A Cancers on a Large Clinical Breast MRI Dataset.

Maryellen L. Giger, Karen Drukker, John Papaioannou, 2019, Academic radiology.

Clinically missed cancer: how effectively can radiologists use computer-aided detection?

John Papaioannou, Robert M Nishikawa, Robert A Schmidt, 2012, AJR. American journal of roentgenology.

Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival “early on” in neoadjuvant treatment of breast cancer

Karen Drukker, Alexandra Edwards, John Papaioannou, 2018, Cancer Imaging.