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.
In
this
section,
AI’s
impact
on
medicine,
specifically
radiation
treatment
processes,
is
highlighted.
AI
in
radiotherapy
has
led
to
significant
innovations,
enhancing
the
precision
and
efficiency
of
cancer
treatments.
Advanced
algorithms
enable
automated
more
accurate
tumor
detection
delineation
imaging,
optimizing
dose
distribution
while
minimizing
exposure
healthy
tissues.
AI-driven
planning
reduces
time
required
for
complex
calculations
improves
personalized
strategies.
Machine
learning
models
predict
patient
responses
potential
side
effects,
allowing
proactive
adjustments.
Overall,
revolutionizing
by
improving
accuracy,
reducing
time,
outcomes.
Digital Health,
Journal Year:
2024,
Volume and Issue:
10
Published: Jan. 1, 2024
This
paper
reviews
the
advancements
in
deep
learning
for
hepatic
vascular
segmentation
and
its
clinical
implications
holistic
management
of
hepatocellular
carcinoma
(HCC).
The
key
to
diagnosis
treatment
HCC
lies
imaging
examinations,
with
challenge
liver
surgery
being
precise
assessment
Hepatic
vasculature.
In
this
regard,
methods,
including
convolutional
neural
networksamong
various
other
approaches,
have
significantly
improved
accuracy
speed.
review
synthesizes
findings
from
30
studies,
covering
aspects
such
as
network
architectures,
applications,
supervision
techniques,
evaluation
metrics,
motivations.
Furthermore,
we
also
examine
challenges
future
prospects
technologies
enhancing
comprehensive
HCC,
discussing
anticipated
breakthroughs
that
could
transform
patient
management.
By
combining
needs
technological
advancements,
is
expected
make
greater
field
segmentation,
thereby
providing
stronger
support
HCC.
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.
