Computers in Biology and Medicine,
Journal Year:
2023,
Volume and Issue:
154, P. 106625 - 106625
Published: Feb. 2, 2023
The
COVID-19
pandemic
has
caused
millions
of
cases
and
deaths
the
AI-related
scientific
community,
after
being
involved
with
detecting
signs
in
medical
images,
been
now
directing
efforts
towards
development
methods
that
can
predict
progression
disease.
This
task
is
multimodal
by
its
very
nature
and,
recently,
baseline
results
achieved
on
publicly
available
AIforCOVID
dataset
have
shown
chest
X-ray
scans
clinical
information
are
useful
to
identify
patients
at
risk
severe
outcomes.
While
deep
learning
superior
performance
several
fields,
most
it
considers
unimodal
data
only.
In
this
respect,
when,
which
how
fuse
different
modalities
an
open
challenge
learning.
To
cope
these
three
questions
here
we
present
a
novel
approach
optimizing
setup
end-to-end
model.
It
exploits
Pareto
multi-objective
optimization
working
metric
diversity
score
multiple
candidate
neural
networks
be
fused.
We
test
our
method
dataset,
attaining
state-of-the-art
results,
not
only
outperforming
but
also
robust
external
validation.
Moreover,
exploiting
XAI
algorithms
figure
out
hierarchy
among
extract
features'
intra-modality
importance,
enriching
trust
predictions
made
BioMed Research International,
Journal Year:
2022,
Volume and Issue:
2022, P. 1 - 22
Published: April 25, 2022
Dense
convolutional
network
(DenseNet)
is
a
hot
topic
in
deep
learning
research
recent
years,
which
has
good
applications
medical
image
analysis.
In
this
paper,
DenseNet
summarized
from
the
following
aspects.
First,
basic
principle
of
introduced;
second,
development
and
analyzed
five
aspects:
broaden
structure,
lightweight
dense
unit,
connection
mode,
attention
mechanism;
finally,
application
field
analysis
three
pattern
recognition,
segmentation,
object
detection.
The
structures
are
systematically
certain
positive
significance
for
DenseNet.
ACM Transactions on Interactive Intelligent Systems,
Journal Year:
2023,
Volume and Issue:
13(4), P. 1 - 35
Published: March 14, 2023
eXplainable
AI
(XAI)
involves
two
intertwined
but
separate
challenges:
the
development
of
techniques
to
extract
explanations
from
black-box
models
and
way
such
are
presented
users,
i.e.,
explanation
user
interface.
Despite
its
importance,
second
aspect
has
received
limited
attention
so
far
in
literature.
Effective
interfaces
fundamental
for
allowing
human
decision-makers
take
advantage
oversee
high-risk
systems
effectively.
Following
an
iterative
design
approach,
we
present
first
cycle
prototyping-testing-redesigning
explainable
technique
interface
clinical
Decision
Support
Systems
(DSS).
We
XAI
that
meets
technical
requirements
healthcare
domain:
sequential,
ontology-linked
patient
data,
multi-label
classification
tasks.
demonstrate
applicability
explain
a
DSS,
prototype
Next,
test
with
providers
collect
their
feedback
two-fold
outcome:
First,
obtain
evidence
increase
users’
trust
system,
second,
useful
insights
on
perceived
deficiencies
interaction
can
re-design
better,
more
human-centered
Radiology Artificial Intelligence,
Journal Year:
2021,
Volume and Issue:
3(6)
Published: Oct. 7, 2021
Data-driven
approaches
have
great
potential
to
shape
future
practices
in
radiology.
The
most
straightforward
strategy
obtain
clinically
accurate
models
is
use
large,
well-curated
and
annotated
datasets.
However,
patient
privacy
constraints,
tedious
annotation
processes,
the
limited
availability
of
radiologists
pose
challenges
building
such
This
review
details
model
training
strategies
scenarios
with
data,
insufficiently
labeled
and/or
expert
resources.
discusses
enlarge
data
sample,
decrease
time
burden
manual
supervised
labeling,
adjust
neural
network
architecture
improve
performance,
apply
semisupervised
approaches,
leverage
efficiencies
from
pretrained
models.
Keywords:
Computer-aided
Detection/Diagnosis,
Transfer
Learning,
Limited
Annotated
Data,
Augmentation,
Synthetic
Semisupervised
Federated
Few-Shot
Class
Imbalance
Diagnostics,
Journal Year:
2021,
Volume and Issue:
11(7), P. 1155 - 1155
Published: June 24, 2021
Since
December
2019,
the
global
health
population
has
faced
rapid
spreading
of
coronavirus
disease
(COVID-19).
With
incremental
acceleration
number
infected
cases,
World
Health
Organization
(WHO)
reported
COVID-19
as
an
epidemic
that
puts
a
heavy
burden
on
healthcare
sectors
in
almost
every
country.
The
potential
artificial
intelligence
(AI)
this
context
is
difficult
to
ignore.
AI
companies
have
been
racing
develop
innovative
tools
contribute
arm
world
against
pandemic
and
minimize
disruption
it
may
cause.
main
objective
study
survey
decisive
role
technology
used
fight
pandemic.
Five
significant
applications
for
were
found,
including
(1)
diagnosis
using
various
data
types
(e.g.,
images,
sound,
text);
(2)
estimation
possible
future
spread
based
current
confirmed
cases;
(3)
association
between
infection
patient
characteristics;
(4)
vaccine
development
drug
interaction;
(5)
supporting
applications.
This
also
introduces
comparison
datasets.
Based
limitations
literature,
review
highlights
open
research
challenges
could
inspire
application
COVID-19.
