Experimenting liver fibrosis diagnostic by two photon excitation microscopy and Bag-of-Features image classification.
about
Mining textural knowledge in biological images: Applications, methods and trendsLabel-free fluorescence lifetime and second harmonic generation imaging microscopy improves quantification of experimental renal fibrosisQuantification of liver fibrosis via second harmonic imaging of the Glisson's capsule from liver surface.Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector.Discriminative Learning for Alzheimer's Disease Diagnosis via Canonical Correlation Analysis and Multimodal Fusion.Longitudinal Analysis for Disease Progression via Simultaneous Multi-Relational Temporal-Fused Learning.Visualizing liver anatomy, physiology and pharmacology using multiphoton microscopy.Systematic quantification of histological patterns shows accuracy in reflecting cirrhotic remodeling.Quantitative imaging of fibrotic and morphological changes in liver of non-alcoholic steatohepatitis (NASH) model mice by second harmonic generation (SHG) and auto-fluorescence (AF) imaging using two-photon excitation microscopy (TPEM).A Study on Image Quality in Polarization-Resolved Second Harmonic Generation Microscopy.Hybrid feature extraction techniques for microscopic hepatic fibrosis classification.Quantitative Morphometry for Osteochondral Tissues Using Second Harmonic Generation Microscopy and Image Texture Information.Hepatic Vitamin A Content Investigation Using Coherent Anti-Stokes Raman Scattering Microscopy.Deep learning enables automated scoring of liver fibrosis stagesQuantitative second harmonic generation microscopy for the structural characterization of capsular collagen in thyroid neoplasms
P2860
Q28077947-5E22788C-3305-4FB9-B52D-8A3888A875A2Q30855908-BB5BDF0D-4AEA-40DB-9F16-40915EB930BCQ35679627-54623AB0-EE4B-469E-949D-2875C4E899D9Q35942846-2F6DC3C3-BC2F-4F1E-A49E-8F8426FD07F4Q36906166-332AD68D-8ACD-4835-A12A-779B929E6EC4Q37678947-8BA6DC1E-30D9-4569-A853-DA65B570D845Q38852027-E8280E0C-CF27-45A7-BBCF-0E4019CC8982Q40379402-62F334A1-6598-483D-9C39-A4B588BA8E49Q41682600-3616F738-C910-47E6-9A88-5ADC5BEB59C2Q47163894-F4FD6E6A-EF2A-4714-B310-14B6E0A1DFF5Q48273136-9591DAA9-82BD-4C19-BAF4-F099257CA729Q49799067-2892F423-5771-4166-BA85-D36A036508DEQ52972773-53FDD33F-06A2-4FE6-8359-669C797BD884Q58099005-DC95741A-6262-4E40-ADC6-D9C8E3BA8C85Q58567700-0D4D39E0-B57C-455C-BBB2-E4B37492A916
P2860
Experimenting liver fibrosis diagnostic by two photon excitation microscopy and Bag-of-Features image classification.
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on 10 April 2014
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
Experimenting liver fibrosis d ...... Features image classification.
@en
Experimenting liver fibrosis d ...... Features image classification.
@nl
type
label
Experimenting liver fibrosis d ...... Features image classification.
@en
Experimenting liver fibrosis d ...... Features image classification.
@nl
prefLabel
Experimenting liver fibrosis d ...... Features image classification.
@en
Experimenting liver fibrosis d ...... Features image classification.
@nl
P2093
P2860
P356
P1433
P1476
Experimenting liver fibrosis d ...... Features image classification.
@en
P2093
Gabor Csucs
George A Stanciu
Peter T C So
Qiwen Peng
Roy E Welsch
Stefan G Stanciu
P2860
P2888
P356
10.1038/SREP04636
P407
P50
P577
2014-04-10T00:00:00Z
P5875
P6179
1005589771