Skip to main navigation
Skip to search
Skip to main content
Heriot-Watt Research Portal Home
Help & FAQ
Link opens in a new tab
Search content at Heriot-Watt Research Portal
Home
Profiles
Research units
Research output
Datasets
Impacts
Equipment
Prizes
Activities
Press/Media
Courses
Fluorescence Lifetime Endomicroscopic Image-based ex-vivo Human Lung Cancer Differentiation Using Machine Learning
Qiang Wang
,
Marta Vallejo
, James Hopgood
School of Engineering & Physical Sciences
Institute of Sensors, Signals & Systems
Research output
:
Working paper
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Fluorescence Lifetime Endomicroscopic Image-based ex-vivo Human Lung Cancer Differentiation Using Machine Learning'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
INIS
images
100%
humans
100%
lifetime
100%
fluorescence
100%
cancer
100%
lungs
100%
machine learning
100%
data
28%
resolution
28%
processing
28%
tissues
14%
size
14%
fibers
14%
patients
14%
excitation
14%
errors
14%
wavelengths
14%
extraction
14%
Engineering
Fluorescence Lifetime
100%
Learning System
100%
Exposure Time
66%
Spatial Resolution
33%
Spectral Resolution
33%
Chemistry
Fluorescence Lifetime
100%