Applied Sciences,
Journal Year:
2023,
Volume and Issue:
13(18), P. 10012 - 10012
Published: Sept. 5, 2023
Image
classification
has
become
highly
significant
in
the
field
of
computer
vision
due
to
its
wide
array
applications.
In
recent
years,
Convolutional
Neural
Networks
(CNN)
have
emerged
as
potent
tools
for
addressing
this
task.
Attention
mechanisms
offer
an
effective
approach
enhance
accuracy
image
classification.
Despite
Global
Average
Pooling
(GAP)
being
a
crucial
component
traditional
attention
mechanisms,
it
only
computes
average
spatial
elements
each
channel,
failing
capture
complete
range
feature
information,
resulting
fewer
and
less
expressive
features.
To
address
limitation,
we
propose
novel
pooling
operation
named
“Binary
Pooling”
integrate
into
block.
Binary
combines
both
GAP
Max
(GMP),
obtaining
more
comprehensive
vector
by
extracting
maximum
values,
thereby
enriching
diversity
extracted
Furthermore,
further
extraction
features,
dilation
operations
pointwise
convolutions
are
applied
on
channel-wise.
The
proposed
block
is
simple
yet
effective.
Upon
integration
ResNet18/50
models,
leads
improvements
2.02%/0.63%
ImageNet.
Cancer Discovery,
Journal Year:
2024,
Volume and Issue:
14(5), P. 711 - 726
Published: March 21, 2024
Artificial
intelligence
(AI)
in
oncology
is
advancing
beyond
algorithm
development
to
integration
into
clinical
practice.
This
review
describes
the
current
state
of
field,
with
a
specific
focus
on
integration.
AI
applications
are
structured
according
cancer
type
and
domain,
focusing
four
most
common
cancers
tasks
detection,
diagnosis,
treatment.
These
encompass
various
data
modalities,
including
imaging,
genomics,
medical
records.
We
conclude
summary
existing
challenges,
evolving
solutions,
potential
future
directions
for
field.
Journal of Radiation Research,
Journal Year:
2023,
Volume and Issue:
65(1), P. 1 - 9
Published: Oct. 19, 2023
This
review
provides
an
overview
of
the
application
artificial
intelligence
(AI)
in
radiation
therapy
(RT)
from
a
oncologist's
perspective.
Over
years,
advances
diagnostic
imaging
have
significantly
improved
efficiency
and
effectiveness
radiotherapy.
The
introduction
AI
has
further
optimized
segmentation
tumors
organs
at
risk,
thereby
saving
considerable
time
for
oncologists.
also
been
utilized
treatment
planning
optimization,
reducing
several
days
to
minutes
or
even
seconds.
Knowledge-based
deep
learning
techniques
employed
produce
plans
comparable
those
generated
by
humans.
Additionally,
potential
applications
quality
control
assurance
plans,
optimization
image-guided
RT
monitoring
mobile
during
treatment.
Prognostic
evaluation
prediction
using
increasingly
explored,
with
radiomics
being
prominent
area
research.
future
oncology
offers
establish
standardization
minimizing
inter-observer
differences
improving
dose
adequacy
evaluation.
through
may
global
implications,
providing
world-standard
resource-limited
settings.
However,
there
are
challenges
accumulating
big
data,
including
patient
background
information
correlating
disease
outcomes.
Although
remain,
ongoing
research
integration
technology
hold
promise
advancements
oncology.
Chinese Medical Journal - Pulmonary and Critical Care Medicine,
Journal Year:
2023,
Volume and Issue:
1(3), P. 148 - 160
Published: Sept. 1, 2023
Lung
cancer
has
the
highest
mortality
rate
among
all
cancers
in
world.
Hence,
early
diagnosis
and
personalized
treatment
plans
are
crucial
to
improving
its
5-year
survival
rate.
Chest
computed
tomography
(CT)
serves
as
an
essential
tool
for
lung
screening,
pathology
images
gold
standard
diagnosis.
However,
medical
image
evaluation
relies
on
manual
labor
suffers
from
missed
or
misdiagnosis,
physician
heterogeneity.
The
rapid
development
of
artificial
intelligence
(AI)
brought
a
whole
novel
opportunity
task
processing,
demonstrating
potential
clinical
application
treatment.
AI
technologies,
including
machine
learning
deep
learning,
have
been
deployed
extensively
nodule
detection,
benign
malignant
classification,
subtype
identification
based
CT
images.
Furthermore,
plays
role
non-invasive
prediction
genetic
mutations
molecular
status
provide
optimal
regimen,
applies
assessment
therapeutic
efficacy
prognosis
patients,
enabling
precision
medicine
become
reality.
Meanwhile,
histology-based
models
assist
pathologists
typing,
characterization,
enhance
efficiency
leap
extensive
still
faces
various
challenges,
such
data
sharing,
standardized
label
acquisition,
regulation,
multimodal
integration.
Nevertheless,
holds
promising
field
improve
care.
Medical Visualization,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 16, 2025
The
fusion
of
artificial
intelligence
with
medical
imaging
is
undoubtedly
a
progressive
innovative
process
in
the
modern
development
domestic
healthcare,
which
allows
for
unprecedented
accuracy
and
efficiency
diagnosis
planning
special
treatment
various
diseases,
including
malignant
tumors.
At
same
time,
approaches,
especially
field
clinical
application
radiotherapy
techniques,
are
spreading
more
widely
moving
from
specialized
research
to
already
accepted
traditional
practice.
Purpose
study:
analyze
approaches
techniques
antitumor
Conclusion.
further
provides
provision
options
prevention,
cancer
patients
against
background
constant
increase
their
implementation,
assistance
optimizing
radiotherapeutic
neoplasms.
