Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging
BioMedInformatics,
Год журнала:
2025,
Номер
5(2), С. 20 - 20
Опубликована: Апрель 14, 2025
Artificial
Intelligence
(AI)
and
deep
learning
models
have
revolutionized
diagnosis,
prognostication,
treatment
planning
by
extracting
complex
patterns
from
medical
images,
enabling
more
accurate,
personalized,
timely
clinical
decisions.
Despite
its
promise,
challenges
such
as
image
heterogeneity
across
different
centers,
variability
in
acquisition
protocols
scanners,
sensitivity
to
artifacts
hinder
the
reliability
integration
of
models.
Addressing
these
issues
is
critical
for
ensuring
accurate
practical
AI-powered
neuroimaging
applications.
We
reviewed
summarized
strategies
improving
robustness
generalizability
segmentation
classification
neuroimages.
This
review
follows
a
structured
protocol,
comprehensively
searching
Google
Scholar,
PubMed,
Scopus
studies
on
neuroimaging,
task-specific
applications,
model
attributes.
Peer-reviewed,
English-language
brain
imaging
were
included.
The
extracted
data
analyzed
evaluate
implementation
effectiveness
techniques.
study
identifies
key
enhance
including
regularization,
augmentation,
transfer
learning,
uncertainty
estimation.
These
approaches
address
major
domain
shifts,
consistent
performance
diverse
settings.
technical
this
can
improve
their
real-world
practice.
Язык: Английский
Neural reshaping: the plasticity of human brain and artificial intelligence in the learning process
American Journal of Neurodegenerative Disease,
Год журнала:
2024,
Номер
13(5), С. 34 - 48
Опубликована: Янв. 1, 2024
This
study
explores
the
concept
of
neural
reshaping
and
mechanisms
through
which
both
human
artificial
intelligence
adapt
learn.
To
investigate
parallels
distinctions
between
brain
plasticity
network
plasticity,
with
a
focus
on
their
learning
processes.
A
comparative
analysis
was
conducted
using
literature
reviews
machine
experiments,
specifically
employing
multi-layer
perceptron
to
examine
regression
classification
problems.
Experimental
findings
demonstrate
that
models,
similar
neuroplasticity,
enhance
performance
iterative
optimization,
drawing
in
strengthening
adjusting
connections.
Understanding
shared
principles
limitations
can
drive
advancements
AI
design
cognitive
neuroscience,
paving
way
for
future
interdisciplinary
innovations.
Язык: Английский