Yulei Jiang - researchr alias

researchr

You are not signed in
Sign in
Sign up

Viewing Publication 1 - 19 from 19

2022

2021

Identification of critical moving characteristics in high speed on/off valve based on time derivative of the coil currentQiang Gao, Yuchuan Zhu, Changwen Wu, Yulei Jiang. jsce, 235(7), 2021. [doi]

2018

Image Segmentation of LBF Model with Variable Coefficient Regularized Area TermLiyan Wang, Jing Liu, Yulei Jiang. csps 2020: 227-235 [doi]

2014

Reference-tissue correction of T2-weighted signal intensity for prostate cancer detectionYahui Peng, Yulei Jiang, Aytekin Oto. micad 2014: 903508 [doi]

Application of artificial neural network and multiple linear regression models for predicting survival time of patients with non-small cell cancer using multiple prognostic factors including FDG-PET measurementsYonglin Pu, Michael J. Baad, Yulei Jiang, Yisheng Chen. ijcnn 2014: 225-230 [doi]

2013

A study of T2-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancerYahui Peng, Yulei Jiang, Tatjana Antic, Maryellen L. Giger, Scott Eggener, Aytekin Oto. micad 2013: [doi]

2012

Computerized image analysis of cell-cell interactions in human renal tissue by using multi-channel immunoflourescent confocal microscopyYahui Peng, Yulei Jiang, Vladimir M. Liarski, Natalya Kaverina, Marcus R. Clark, Maryellen L. Giger. micad 2012: [doi]

2011

Segmentation of prostatic glands in histology imagesYahui Peng, Yulei Jiang, Laurie Eisengart, Mark A. Healy, Francis H. Straus, Ximing J. Yang. isbi 2011: 2091-2094 [doi]

2009

A study of the effect of noise injection on the training of artificial neural networksYulei Jiang, Richard M. Zur, Lorenzo L. Pesce, Karen Drukker. ijcnn 2009: 1428-1432 [doi]

2007

Uncertainty in the Output of Artificial Neural NetworksYulei Jiang. ijcnn 2007: 2551-2556 [doi]

2005

A study of several CAD methods for classification of clustered microcalcificationsLiyang Wei, Yongyi Yang, Robert M. Nishikawa, Yulei Jiang. miip 2005: [doi]

A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcificationsLiyang Wei, Yongyi Yang, Robert M. Nishikawa, Yulei Jiang. tmi, 24(3):371-380, 2005. [doi]

2003

Uncertainty in the Output of Artificial Neural NetworksYulei Jiang. tmi, 22(7):913-921, 2003.

Automated selection of BI-RADS lesion descriptors for reporting calcifications in mammogramsSophie Paquerault, Yulei Jiang, Robert M. Nishikawa, Robert A. Schmidt, Carl J. D'Orsi, Carl J. Vyborny, Gillian M. Newstead. miip 2003: [doi]

2002

Improving the automated classification of clustered calcifications on mammograms through the improved detection of individual calcificationsRobert M. Nishikawa, Maria Fernanda Salfity, Yulei Jiang, John Papaioannou. miip 2002: [doi]

2001

Eliminating false-positive microcalcification clusters in a mammography CAD scheme using a Bayesian neural networkDarrin C. Edwards, John Papaioannou, Yulei Jiang, Matthew A. Kupinski, Robert M. Nishikawa. miip 2001: [doi]

1998

Requirement of microcalcification detection for computerized classification of malignant and benign clustered microcalcificationsYulei Jiang, Robert M. Nishikawa, John Papaioannou. miip 1998: [doi]

Benefits of Computer-Aided Diagnosis (CAD) in Mammographic Diagnosis of Malignant and Benign Clustered MicrocalcificationsYulei Jiang, Robert M. Nishikawa, Robert A. Schmidt, Charles E. Metz, Maryellen L. Giger, Kunio Doi. mammo 1998: 215-220 [doi]

Sign in or sign up to see more results.

runs on WebDSL