Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images.
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Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging.Radiomics: Images Are More than Pictures, They Are Data.Color-coded visualization of magnetic resonance imaging multiparametric maps.Detection of prostate cancer in multiparametric MRI using random forest with instance weighting.Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications.Texture analysis of medical images for radiotherapy applications.Unsupervised Clustering of Quantitative Image Phenotypes Reveals Breast Cancer Subtypes with Distinct Prognoses and Molecular Pathways.Computer aided diagnosis of prostate cancer: A texton based approach.Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.Characterization and stratification of prostate lesions based on comprehensive multiparametric MRI using detailed whole-mount histopathology as a reference standard.Prostate cancer radiomics and the promise of radiogenomics.Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.Radiomics and radiogenomics for precision radiotherapy.A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images.Preoperative prediction of muscular invasiveness of bladder cancer with radiomic features on conventional MRI and its high-order derivative maps.Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI.Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings.Prostate cancer: The applicability of textural analysis of MRI for grading.Molecular imaging of prostate cancer.MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy.[MRI of the prostate].T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results.Photoacoustic imaging with an acoustic lens detects prostate cancer cells labeled with PSMA-targeting near-infrared dye-conjugates.Assessing the inter-observer variability of Computer-Aided Nodule Assessment and Risk Yield (CANARY) to characterize lung adenocarcinomas.The Role of PET-Based Radiomic Features in Predicting Local Control of Esophageal Cancer Treated with Concurrent Chemoradiotherapy.A Supervised Learning Tool for Prostate Cancer Foci Detection and Aggressiveness Identification using Multiparametric magnetic resonance imaging/magnetic resonance spectroscopy imaging.Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapyPET/MRI and prostate cancerIdentifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findingsAutomatic Detection of Prostate Tumor Habitats using Diffusion MRI
P2860
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P2860
Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images.
description
2015 nî lūn-bûn
@nan
2015年の論文
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2015年学术文章
@wuu
2015年学术文章
@zh-cn
2015年学术文章
@zh-hans
2015年学术文章
@zh-my
2015年学术文章
@zh-sg
2015年學術文章
@yue
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2015年學術文章
@zh-hant
name
Automatic classification of pr ...... ric magnetic resonance images.
@ast
Automatic classification of pr ...... ric magnetic resonance images.
@en
type
label
Automatic classification of pr ...... ric magnetic resonance images.
@ast
Automatic classification of pr ...... ric magnetic resonance images.
@en
prefLabel
Automatic classification of pr ...... ric magnetic resonance images.
@ast
Automatic classification of pr ...... ric magnetic resonance images.
@en
P2093
P2860
P50
P356
P1476
Automatic classification of pr ...... ric magnetic resonance images.
@en
P2093
Herbert Alberto Vargas
Kazuhiro Matsumoto
Tatsuo Gondo
P2860
P304
P356
10.1073/PNAS.1505935112
P407
P577
2015-11-02T00:00:00Z