Prostate cancer classification with multiparametric MRI transfer learning model
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Prostate cancer classification with multiparametric MRI transfer learning model
Authors
Keywords
-
Journal
MEDICAL PHYSICS
Volume -, Issue -, Pages -
Publisher
Wiley
Online
2019-01-01
DOI
10.1002/mp.13367
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI
- (2018) Yang Song et al. JOURNAL OF MAGNETIC RESONANCE IMAGING
- A Deep Active Survival Analysis approach for precision treatment recommendations: Application of prostate cancer
- (2018) Milad Zafar Nezhad et al. EXPERT SYSTEMS WITH APPLICATIONS
- Machine Learning to Predict Postradical Prostatectomy Pathology Outcomes in Intermediate Risk Prostate Cancer
- (2017) J. Kang et al. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
- Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data
- (2017) Justin Guinney et al. LANCET ONCOLOGY
- Deep learning for polyp recognition in wireless capsule endoscopy images
- (2017) Yixuan Yuan et al. MEDICAL PHYSICS
- Dermatologist-level classification of skin cancer with deep neural networks
- (2017) Andre Esteva et al. NATURE
- Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain
- (2017) Ruikai Zhang et al. IEEE Journal of Biomedical and Health Informatics
- Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning
- (2017) Xinggang Wang et al. Scientific Reports
- Computer-aided Detection of Prostate Cancer with MRI
- (2016) Lizhi Liu et al. ACADEMIC RADIOLOGY
- AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
- (2016) Shadi Albarqouni et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images
- (2016) Phillip M. Cheng et al. JOURNAL OF DIGITAL IMAGING
- Haralick textural features onT2-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer
- (2016) Khémara Gnep et al. JOURNAL OF MAGNETIC RESONANCE IMAGING
- Radiomics: Images Are More than Pictures, They Are Data
- (2016) Robert J. Gillies et al. RADIOLOGY
- Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores
- (2015) Andreas Wibmer et al. EUROPEAN RADIOLOGY
- Genetic Variants in Fanconi Anemia Pathway Genes BRCA2 and FANCA Predict Melanoma Survival
- (2015) Jieyun Yin et al. JOURNAL OF INVESTIGATIVE DERMATOLOGY
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness
- (2015) A Vignati et al. PHYSICS IN MEDICINE AND BIOLOGY
- Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images
- (2015) Duc Fehr et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Kernel-Based Learning From Both Qualitative and Quantitative Labels: Application to Prostate Cancer Diagnosis Based on Multiparametric MR Imaging
- (2014) Emilie Niaf et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Computer-Aided Detection of Prostate Cancer in MRI
- (2014) Geert Litjens et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T
- (2014) Fusun Citak-Er et al. Biomed Research International
- Active learning on manifolds
- (2013) Cheng Li et al. NEUROCOMPUTING
- Quantitative Analysis of Multiparametric Prostate MR Images: Differentiation between Prostate Cancer and Normal Tissue and Correlation with Gleason Score—A Computer-aided Diagnosis Development Study
- (2013) Yahui Peng et al. RADIOLOGY
- Upgrading and Downgrading of Prostate Cancer from Biopsy to Radical Prostatectomy: Incidence and Predictive Factors Using the Modified Gleason Grading System and Factoring in Tertiary Grades
- (2012) Jonathan I. Epstein et al. EUROPEAN UROLOGY
- Diffusion-Weighted and Dynamic Contrast-Enhanced MRI of Prostate Cancer: Correlation of Quantitative MR Parameters With Gleason Score and Tumor Angiogenesis
- (2011) Aytekin Oto et al. AMERICAN JOURNAL OF ROENTGENOLOGY
- MR Imaging of Treated Prostate Cancer
- (2011) Hebert Alberto Vargas et al. RADIOLOGY
- Is Apparent Diffusion Coefficient Associated with Clinical Risk Scores for Prostate Cancers that Are Visible on 3-T MR Images?
- (2010) Baris Turkbey et al. RADIOLOGY
- A Survey on Transfer Learning
- (2009) Sinno Jialin Pan et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search