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m
o
g
r
ap
h
y
,
m
ag
n
etic
r
eso
n
an
ce
im
ag
in
g
,
an
d
p
o
s
itro
n
em
is
s
io
n
to
m
o
g
r
ap
h
y
,
is
g
en
er
ally
lab
o
r
-
in
ten
s
iv
e,
tim
e
-
co
n
s
u
m
i
n
g
,
an
d
ass
o
ciate
d
with
s
ig
n
if
ican
t
in
ter
an
d
in
tr
a
-
o
b
s
er
v
er
v
ar
iab
ilit
y
.
T
h
is
led
r
esear
ch
er
s
to
d
ev
el
o
p
r
eliab
le
im
ag
e
s
eg
m
en
tatio
n
m
eth
o
d
s
th
at
ar
e
au
to
m
ated
,
r
ep
r
o
d
u
ci
b
le,
an
d
ac
cu
r
ate.
Dee
p
lear
n
in
g
,
in
p
a
r
ticu
lar
co
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
s
,
is
r
ec
o
g
n
ized
as
a
b
r
ea
k
th
r
o
u
g
h
i
n
im
ag
e
s
eg
m
en
tatio
n
,
a
s
th
ey
ar
e
ef
f
ec
tiv
e
in
d
ea
lin
g
with
lo
ca
l
f
ea
tu
r
e
s
an
d
s
p
atial
h
ier
ar
ch
ies
in
i
m
ag
es.
Ho
wev
er
,
th
e
i
n
h
er
en
t
lim
itatio
n
s
o
f
th
e
co
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
s
ar
e
in
d
ea
lin
g
with
th
e
m
o
d
elin
g
o
f
lo
n
g
-
r
an
g
e
d
ep
en
d
en
cies,
wh
ich
ar
e
im
p
o
r
tan
t in
t
h
e
ac
cu
r
ate
s
eg
m
en
tatio
n
o
f
h
eter
o
g
en
eo
u
s
tu
m
o
r
r
e
g
io
n
s
with
ir
r
eg
u
lar
b
o
u
n
d
ar
ies
[
1
]
.
R
ec
en
t
liter
atu
r
e
also
r
ef
lects
th
e
p
atter
n
in
t
h
e
p
r
o
g
r
ess
o
f
th
e
f
ield
’
s
ad
v
an
ce
m
en
t.
Fo
r
ex
am
p
le,
th
e
UNe
t++
ar
ch
itectu
r
e,
wh
i
ch
is
an
e
x
ten
s
io
n
o
f
th
e
U
-
N
et
ar
ch
itectu
r
e,
h
as
b
ee
n
ap
p
lied
f
o
r
liv
er
t
u
m
o
r
s
eg
m
en
tatio
n
u
s
in
g
d
en
s
e
s
k
ip
co
n
n
ec
tio
n
s
an
d
m
u
lti
-
s
c
a
le
f
e
a
tu
r
e
a
g
g
r
e
g
a
tio
n
te
c
h
n
iq
u
e
s
[
2
]
.
Oth
er
r
ec
en
t
s
tu
d
ies
h
av
e
f
o
cu
s
ed
o
n
th
e
d
ev
elo
p
m
en
t
o
f
u
n
i
f
ied
d
ee
p
le
ar
n
in
g
m
o
d
els
f
o
r
in
teg
r
atin
g
s
eg
m
en
tatio
n
with
o
th
er
task
s
s
u
ch
as
class
if
ica
tio
n
in
co
m
p
u
ted
to
m
o
g
r
a
p
h
y
(
CT
)
im
ag
in
g
s
tu
d
ies
[
3
]
.
R
FiLM
U
-
Net
an
d
MAP
F
UNe
t
in
co
r
p
o
r
ate
r
ad
io
m
ic
f
eatu
r
es
an
d
f
u
s
io
n
u
s
in
g
m
u
lt
i
-
atten
ti
o
n
tech
n
i
q
u
es
f
o
r
imp
r
o
v
ed
b
o
u
n
d
ar
y
s
eg
m
e
n
tatio
n
.
Dete
ctio
n
-
b
ased
ap
p
r
o
ac
h
es
u
s
in
g
YOL
Ov
8
an
d
lear
n
in
g
ap
p
r
o
ac
h
es
b
ased
o
n
p
atch
-
b
ased
m
eth
o
d
s
f
o
r
lim
i
ted
d
atasets
[
4
]
,
as
well
a
s
s
p
atio
tem
p
o
r
al
lear
n
in
g
u
s
in
g
t
h
e
C
UNe
t
-
C
L
ST
M
m
o
d
el,
h
av
e
also
b
ee
n
p
r
o
p
o
s
ed
.
Mo
r
e
r
ec
en
t
s
tu
d
ies
h
av
e
f
o
cu
s
ed
o
n
th
e
p
o
te
n
tial
b
en
ef
its
o
f
u
s
in
g
tr
an
s
f
o
r
m
er
s
in
C
NNs f
o
r
liv
e
r
tu
m
o
r
s
eg
m
en
tatio
n
u
s
in
g
R
esTr
an
s
UNe
t a
n
d
MM
E
FU
-
N
e
t.
Desp
ite
th
e
s
ig
n
if
ican
t
a
d
v
a
n
ce
m
en
ts
r
ec
o
r
d
ed
b
y
th
ese
r
ec
en
t
s
tu
d
ies,
a
s
ig
n
if
ica
n
t
g
ap
in
co
m
p
r
eh
e
n
s
iv
e
r
ev
iews
th
at
s
p
ec
if
ically
in
teg
r
ate
r
ec
e
n
t
ad
v
an
ce
s
in
h
y
b
r
id
C
NN
-
tr
an
s
f
o
r
m
er
ar
c
h
itectu
r
es
f
o
r
liv
er
tu
m
o
r
s
eg
m
en
tatio
n
task
s
is
ev
id
en
t.
Alth
o
u
g
h
p
r
e
v
io
u
s
r
ev
iews
p
r
o
v
id
e
a
g
en
er
a
l
o
v
er
v
iew
o
f
d
ee
p
lear
n
in
g
tech
n
iq
u
es
in
m
ed
ica
l
im
ag
in
g
an
al
y
s
is
,
th
ey
o
f
ten
f
o
cu
s
m
o
r
e
o
n
C
NN
-
b
ased
tec
h
n
iq
u
es
o
r
g
en
e
r
ic
tr
an
s
f
o
r
m
er
m
o
d
els
an
d
t
h
er
e
f
o
r
e
f
ail
to
r
e
p
r
esen
t
ad
e
q
u
at
ely
th
e
p
o
ten
tial
s
y
n
er
g
y
b
etwe
en
h
y
b
r
i
d
m
o
d
els
d
ev
elo
p
e
d
b
etwe
en
2
0
2
3
an
d
2
0
2
5
.
Mo
r
eo
v
er
,
m
o
s
t
ex
is
tin
g
r
ev
iews
ar
e
d
escr
ip
tiv
e
i
n
n
atu
r
e
an
d
p
r
im
ar
il
y
f
o
cu
s
o
n
p
r
o
v
id
in
g
a
co
m
p
r
e
h
en
s
iv
e
o
v
e
r
v
iew
o
f
a
r
ch
itect
u
r
es
in
s
tead
o
f
a
r
ig
o
r
o
u
s
co
m
p
ar
ativ
e
an
al
y
s
is
b
ased
o
n
q
u
a
n
titativ
e
m
etr
ics
s
u
ch
as
Dice
s
co
r
es
a
n
d
ev
a
lu
atio
n
u
s
in
g
s
tan
d
ar
d
m
etr
ic
s
s
u
ch
as
L
iTS
an
d
3
DI
R
C
ADb
d
atasets
.
T
h
er
ef
o
r
e,
in
t
h
is
s
tu
d
y
,
a
s
y
s
tem
atic
m
eth
o
d
o
lo
g
y
is
f
o
l
lo
wed
to
p
r
o
v
id
e
a
co
m
p
r
e
h
en
s
iv
e
an
d
an
aly
ti
cal
o
v
e
r
v
iew
o
f
r
ece
n
t
ad
v
a
n
cem
en
ts
in
h
y
b
r
id
C
NN
-
tran
s
f
o
r
m
er
ar
c
h
it
ectu
r
es
f
o
r
li
v
e
r
tu
m
o
r
s
eg
m
en
tatio
n
task
s
.
T
h
e
m
ain
co
n
tr
ib
u
tio
n
s
o
f
th
is
s
tu
d
y
a
r
e:
a.
A
n
o
v
el
tax
o
n
o
m
y
:
a
n
o
v
el
tax
o
n
o
m
y
is
p
r
o
p
o
s
ed
b
y
class
if
y
in
g
r
ec
en
t
s
tate
-
of
-
th
e
-
ar
t
m
o
d
els
b
ased
o
n
d
is
tin
ct
h
y
b
r
id
izatio
n
s
tr
ateg
ies s
u
ch
as ser
ial,
p
ar
allel,
an
d
m
u
lti
-
s
ca
le
f
u
s
io
n
.
b.
Per
f
o
r
m
an
ce
b
e
n
ch
m
ar
k
in
g
:
a
r
ig
o
r
o
u
s
co
m
p
a
r
ativ
e
an
aly
s
is
o
f
r
ec
en
t
m
o
d
els
d
ev
elo
p
ed
b
etwe
en
2
0
2
3
an
d
2
0
2
5
is
p
r
o
v
id
e
d
b
y
co
m
p
ar
in
g
co
m
p
u
tatio
n
al
e
f
f
ic
ie
n
c
y
,
Dice
s
co
r
es,
an
d
ar
c
h
itectu
r
a
l in
n
o
v
atio
n
s
.
c.
Me
th
o
d
o
lo
g
ical
r
ig
o
r
:
u
n
lik
e
p
r
ev
io
u
s
r
ev
iews
th
at
p
r
im
ar
i
ly
p
r
o
v
id
e
a
d
escr
ip
tiv
e
a
n
aly
s
is
b
ased
o
n
a
g
en
er
al
o
v
er
v
iew
o
f
ar
ch
itectu
r
es
an
d
tec
h
n
iq
u
es,
th
is
s
tu
d
y
f
o
llo
ws
a
r
ig
o
r
o
u
s
m
eth
o
d
o
lo
g
y
b
ased
o
n
th
e
PR
I
SMA
2
0
2
0
g
u
id
elin
es.
d.
Fu
tu
r
e
r
o
ad
m
ap
:
em
er
g
in
g
tr
e
n
d
s
s
u
ch
as
f
ed
e
r
ated
lear
n
in
g
an
d
ex
p
lain
ab
le
a
r
tific
ial
in
te
llig
en
ce
(
AI
)
in
th
e
co
n
tex
t
o
f
h
y
b
r
id
m
o
d
els
ar
e
d
is
cu
s
s
ed
in
th
is
s
tu
d
y
t
o
o
v
e
r
co
m
e
is
s
u
es
s
u
ch
as
in
ter
p
r
etab
ilit
y
in
r
ec
en
t h
y
b
r
id
m
o
d
els.
T
h
e
s
p
ec
if
ic
tim
e
f
r
a
m
e
o
f
2
0
2
3
-
2
0
2
5
c
ap
tu
r
es
t
h
e
“
mo
s
t
in
ten
s
ive
p
h
a
s
e
o
f
h
yb
r
id
C
N
N
-
t
r
a
n
s
fo
r
mer
in
n
o
va
tio
n
in
liver tu
mo
r
s
eg
men
ta
tio
n
.
”
2.
B
ACK
G
RO
UND
A
N
D
P
R
E
LI
MI
N
A
R
I
ES
L
iv
er
ca
n
ce
r
,
s
p
ec
if
ically
HC
C
,
is
a
m
ajo
r
p
u
b
lic
h
ea
lth
p
r
o
b
lem
an
d
a
m
ajo
r
c
o
n
tr
ib
u
to
r
to
ca
n
ce
r
-
r
elate
d
d
ea
th
s
.
I
n
th
e
co
n
te
x
t
o
f
HC
C
d
iag
n
o
s
is
,
it
is
cr
itical
to
n
o
te
th
at
th
e
e
ar
ly
d
etec
tio
n
o
f
liv
er
ca
n
ce
r
is
h
in
d
er
ed
b
y
t
h
e
lack
o
f
s
p
ec
if
ic
an
d
s
ig
n
if
ican
t
clin
ical
s
y
m
p
to
m
s
d
u
r
in
g
t
h
e
ea
r
ly
s
t
ag
es.
T
h
ese
asp
ec
ts
h
ig
h
lig
h
t
th
e
n
ee
d
to
d
e
v
elo
p
ac
cu
r
ate
an
d
tim
ely
d
iag
n
o
s
t
ic
to
o
ls
to
as
s
is
t
in
d
ec
is
io
n
-
m
ak
in
g
.
C
o
m
p
u
te
d
to
m
o
g
r
a
p
h
y
(
C
T
)
,
m
ag
n
etic
r
eso
n
an
ce
im
ag
i
n
g
(
MRI)
,
an
d
p
o
s
itro
n
em
is
s
io
n
to
m
o
g
r
ap
h
y
(
PET
)
p
lay
a
v
ital
r
o
le
in
th
e
d
iag
n
o
s
is
an
d
tr
ea
t
m
en
t
o
f
liv
er
ca
n
ce
r
.
I
n
th
e
c
o
n
tex
t
o
f
m
ed
ical
im
ag
e
an
al
y
s
is
,
m
ed
ical
im
ag
e
s
eg
m
en
tatio
n
is
co
n
s
id
er
ed
to
b
e
an
ess
en
tial
s
tep
in
th
e
q
u
a
n
titativ
e
an
aly
s
is
o
f
m
ed
ical
im
ag
es.
I
n
th
e
co
n
tex
t
o
f
liv
er
an
d
liv
er
tu
m
o
r
s
eg
m
en
tatio
n
,
it
is
cr
itical
t
o
n
o
te
th
at
ac
cu
r
ate
s
eg
m
en
tat
io
n
o
f
th
e
liv
er
a
n
d
tu
m
o
r
s
p
lay
s
a
v
ital
r
o
le
in
v
o
lu
m
etr
ic
an
aly
s
is
an
d
tr
ea
t
m
en
t
p
lan
n
in
g
.
T
r
ad
itio
n
ally
,
m
an
u
al
an
d
s
em
i
-
au
to
m
ated
m
et
h
o
d
s
wer
e
ad
o
p
ted
to
s
eg
m
e
n
t
liv
er
an
d
tu
m
o
r
s
.
T
h
ese
m
eth
o
d
s
wer
e
f
o
u
n
d
to
b
e
h
ig
h
ly
lab
o
r
-
in
ten
s
iv
e
an
d
to
o
k
a
s
ig
n
if
ic
an
t
am
o
u
n
t
o
f
tim
e.
I
n
ad
d
it
io
n
,
th
ese
m
eth
o
d
s
wer
e
also
f
o
u
n
d
to
ex
h
ib
it
s
ig
n
if
ican
t
in
tr
a
-
a
n
d
in
te
r
-
o
b
s
er
v
er
v
ar
iab
ilit
y
.
I
n
th
e
co
n
t
ex
t
o
f
c
o
m
p
u
ter
-
b
ased
m
eth
o
d
s
,
in
itial
m
eth
o
d
s
ad
o
p
ted
t
o
s
eg
m
en
t
m
e
d
ical
i
m
ag
es
wer
e
f
o
u
n
d
t
o
ex
h
i
b
it
s
ig
n
ifican
t
li
m
it
ation
s
in
h
an
d
li
n
g
v
ar
ia
b
il
it
y
in
liv
er
an
d
tu
m
o
r
s
h
a
p
es.
T
h
ese
lim
itatio
n
s
h
av
e
led
to
a
g
r
ad
u
al
m
o
v
e
f
r
o
m
m
ac
h
in
e
lear
n
i
n
g
-
b
ased
m
eth
o
d
s
to
d
ee
p
lear
n
in
g
-
b
ased
m
eth
o
d
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
:
1
3
8
2
-
1
3
9
8
1384
2
.
1
.
E
v
a
lua
t
io
n
m
e
t
rics
T
h
e
o
b
jectiv
e
ass
ess
m
en
t
o
f
th
e
p
er
f
o
r
m
a
n
ce
o
f
s
eg
m
en
ta
tio
n
is
ca
r
r
ie
d
o
u
t
t
h
r
o
u
g
h
s
tan
d
ar
d
ized
q
u
an
titativ
e
m
ea
s
u
r
es.
I
n
th
e
co
n
tex
t
o
f
m
ed
ical
im
ag
e
s
eg
m
en
tatio
n
r
esear
ch
,
th
e
m
o
s
t
f
r
eq
u
en
tly
u
s
ed
m
ea
s
u
r
es
o
f
e
v
alu
atio
n
o
f
s
eg
m
en
tatio
n
p
e
r
f
o
r
m
an
ce
h
av
e
b
ee
n
:
d
ice
s
im
ilar
ity
c
o
ef
f
icie
n
t
(
DSC
)
:
T
h
e
Dice
Similar
ity
C
o
ef
f
icien
t
m
ea
s
u
r
es
th
e
s
p
atial
o
v
er
lap
b
etwe
en
th
e
p
r
ed
icted
s
eg
m
e
n
tatio
n
an
d
th
e
r
ef
er
en
ce
g
r
o
u
n
d
tr
u
th
,
with
v
alu
es
b
e
twee
n
0
an
d
1
.
I
n
ter
s
ec
tio
n
o
v
er
u
n
io
n
(
I
o
U)
:
T
h
e
I
n
ter
s
ec
tio
n
o
v
e
r
Un
io
n
m
ea
s
u
r
es
th
e
r
atio
o
f
t
h
e
in
t
er
s
ec
tio
n
an
d
u
n
io
n
o
f
th
e
p
r
ed
icted
an
d
r
ef
e
r
en
ce
s
eg
m
e
n
tatio
n
,
p
r
o
v
id
in
g
a
d
if
f
er
en
t
m
ea
s
u
r
e
o
f
v
o
l
u
m
e
tr
ic
ag
r
ee
m
en
t
b
etwe
en
th
e
two
.
Hau
s
d
o
r
f
f
d
is
tan
ce
(
H
D
)
:
T
h
e
H
a
u
s
d
o
r
f
f
Dis
tan
ce
m
ea
s
u
r
es
th
e
m
ax
i
m
u
m
d
is
tan
ce
b
etwe
en
th
e
b
o
u
n
d
ar
y
p
o
in
ts
o
f
th
e
p
r
e
d
icted
an
d
r
ef
e
r
en
ce
co
n
to
u
r
s
.
T
h
ese
m
ea
s
u
r
es p
r
o
v
id
e
d
if
f
e
r
en
t v
iews o
f
t
h
e
q
u
a
lity
o
f
th
e
s
eg
m
e
n
tatio
n
p
e
r
f
o
r
m
an
ce
.
2
.
2
.
B
enchm
a
r
k
da
t
a
s
et
s
Pro
g
r
ess
in
liv
er
an
d
liv
er
tu
m
o
r
s
eg
m
en
tatio
n
h
as
b
ee
n
f
ac
ilit
ated
b
y
th
e
av
ailab
ilit
y
o
f
p
u
b
licly
ac
ce
s
s
ib
le
b
en
ch
m
ar
k
d
atasets
with
ex
p
er
t a
n
n
o
tatio
n
s
.
C
o
m
m
o
n
ly
u
s
ed
d
atasets
in
th
is
d
o
m
ain
in
clu
d
e:
a.
L
iv
er
tu
m
o
r
s
eg
m
en
tatio
n
ch
a
llen
g
e
(
L
iTS
):
A
wid
ely
a
d
o
p
ted
d
ataset
co
m
p
r
is
in
g
c
o
n
tr
a
s
t
-
en
h
an
ce
d
C
T
s
ca
n
s
with
an
n
o
tatio
n
s
f
o
r
liv
e
r
an
d
tu
m
o
r
r
eg
io
n
s
.
b.
3
DI
R
C
ADb
:
A
co
llectio
n
o
f
h
ig
h
-
r
eso
lu
tio
n
3
D
C
T
v
o
lu
m
es
with
m
an
u
ally
s
eg
m
e
n
ted
l
iv
er
an
d
tu
m
o
r
m
ask
s
,
in
clu
d
in
g
ca
s
es with
c
o
m
p
lex
a
n
d
m
u
ltifo
ca
l tu
m
o
r
s
.
c.
C
HAOS
:
A
m
u
lti
-
m
o
d
al
d
ataset
th
at
in
clu
d
es
C
T
an
d
MRI
(
T
1
-
DUAL
an
d
T
2
-
SP
I
R
)
im
ag
es,
s
u
p
p
o
r
tin
g
cr
o
s
s
-
m
o
d
ality
liv
er
s
eg
m
e
n
ta
tio
n
s
tu
d
ies.
d.
SLI
V
E
R
0
7
a
n
d
B
T
C
V:
Ad
d
i
ti
o
n
al
C
T
-
b
ase
d
d
at
asets
f
r
e
q
u
e
n
tl
y
u
s
e
d
f
o
r
li
v
e
r
s
e
g
m
e
n
t
ati
o
n
b
e
n
c
h
m
a
r
k
in
g
an
d
v
ali
d
a
ti
o
n
.
e.
M
e
d
i
c
a
l
s
e
g
m
e
n
t
at
i
o
n
d
e
c
at
h
l
o
n
(
M
S
D
)
–
l
i
v
e
r
ta
s
k
:
A
s
u
b
s
e
t
o
f
t
h
e
MS
D
c
h
al
l
e
n
g
e
p
r
o
v
i
d
in
g
C
T
v
o
l
u
m
es
w
i
t
h
l
i
v
e
r
a
n
d
t
u
m
o
r
a
n
n
o
t
a
t
i
o
n
s
.
T
h
ese
d
atasets
en
ab
le
co
m
p
ar
ativ
e
an
aly
s
is
o
f
s
eg
m
en
ta
tio
n
ap
p
r
o
ac
h
es
u
n
d
er
d
i
v
er
s
e
im
ag
in
g
co
n
d
itio
n
s
an
d
an
ato
m
ical
v
a
r
iatio
n
s
.
I
t
is
im
p
o
r
tan
t
to
n
o
te
th
at
r
ep
o
r
te
d
r
esu
lts
ac
r
o
s
s
s
tu
d
ies
ar
e
o
f
ten
o
b
tain
ed
u
s
in
g
d
if
f
er
en
t
ex
p
e
r
im
en
tal
p
r
o
to
c
o
ls
,
p
r
ep
r
o
ce
s
s
in
g
s
tr
ateg
ies,
an
d
d
ata
s
p
lits
,
wh
ich
m
ay
af
f
ec
t
d
ir
ec
t c
o
m
p
a
r
ab
ilit
y
.
3.
T
AXO
NO
M
Y
O
F
DE
E
P
L
E
ARNING
-
B
ASE
D
L
I
VE
R
A
ND
T
U
M
O
R
SE
G
M
E
NT
A
T
I
O
N
M
O
DE
L
S
T
h
e
r
ap
id
e
v
o
lu
tio
n
o
f
d
ee
p
l
ea
r
n
in
g
h
as
r
esu
lted
in
a
d
iv
e
r
s
e
r
an
g
e
o
f
ar
ch
itectu
r
es
f
o
r
liv
er
an
d
liv
er
tu
m
o
r
s
eg
m
e
n
tatio
n
.
T
o
p
r
o
v
id
e
a
s
tr
u
ctu
r
ed
o
v
er
v
iew
o
f
th
is
lan
d
s
ca
p
e,
th
is
r
ev
iew
o
r
g
an
izes
r
ep
r
esen
tativ
e
ap
p
r
o
ac
h
es
in
to
a
tax
o
n
o
m
y
b
ased
o
n
t
h
eir
u
n
d
e
r
ly
in
g
a
r
ch
itectu
r
al
p
r
in
cip
les.
T
h
e
class
if
icatio
n
r
ef
lects
b
o
th
c
o
n
ce
p
tu
al
d
esig
n
ch
o
ices
an
d
h
is
to
r
ical
d
ev
elo
p
m
en
t
tr
e
n
d
s
with
in
th
e
f
ield
,
r
an
g
in
g
f
r
o
m
co
n
v
en
tio
n
al
co
n
v
o
lu
tio
n
al
m
o
d
els
to
m
o
r
e
r
ec
en
t
h
y
b
r
id
a
n
d
m
u
ltimo
d
al
s
y
s
tem
s
.
T
h
e
m
ain
ca
teg
o
r
ies
co
n
s
id
er
ed
i
n
th
i
s
r
ev
iew
ar
e:
i
)
C
NN
-
b
ased
m
o
d
els,
ii
)
atten
tio
n
-
e
n
h
an
ce
d
C
NN
m
o
d
els,
iii
)
h
y
b
r
id
C
NN
–
t
r
an
s
f
o
r
m
e
r
m
o
d
els,
iv
)
tr
an
s
f
o
r
m
e
r
-
o
n
ly
m
o
d
els,
an
d
v
)
m
u
ltimo
d
al
an
d
en
s
em
b
le
ap
p
r
o
ac
h
es.
Fig
u
r
e
1
illu
s
tr
ates a
n
o
v
er
v
iew
o
f
th
e
p
r
o
p
o
s
ed
tax
o
n
o
m
y
.
Fig
u
r
e
1
.
T
a
x
o
n
o
m
y
o
f
d
ee
p
l
ea
r
n
in
g
-
b
ased
m
ed
ical
im
a
g
e
s
eg
m
en
tatio
n
m
o
d
els
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Hyb
r
id
co
n
vo
lu
tio
n
a
l n
e
u
r
a
l
n
etw
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r
k
–
tr
a
n
s
fo
r
mer m
o
d
els f
o
r
…
(
I
b
r
a
h
im
Mo
h
a
med
A
ttiy
a
)
1385
3
.
1
.
CNN
-
ba
s
ed
m
o
dels
C
o
n
v
o
lu
tio
n
al
n
e
u
r
al
n
etwo
r
k
s
(
C
NN
s
)
f
o
r
m
th
e
b
asis
o
f
th
e
d
o
m
in
an
t
ap
p
r
o
ac
h
to
m
o
d
e
r
n
m
ed
ical
im
ag
e
s
eg
m
en
tatio
n
task
s
.
T
h
eir
s
u
cc
ess
is
attr
ib
u
te
d
t
o
in
d
u
ctiv
e
b
iases
ap
p
r
o
p
r
ia
te
f
o
r
im
ag
e
d
ata,
in
clu
d
in
g
tr
a
n
s
latio
n
in
v
ar
ian
c
e
an
d
h
ier
ar
c
h
ical
lo
ca
l f
ea
tu
r
e
lear
n
in
g
u
s
in
g
co
n
v
o
l
u
tio
n
o
p
er
atio
n
s
.
Fo
r
liv
er
tu
m
o
r
s
eg
m
en
tatio
n
,
en
c
o
d
er
-
d
ec
o
d
e
r
ar
ch
itectu
r
es
lik
e
th
e
p
o
p
u
lar
U
-
Net
an
d
its
v
ar
ian
ts
ar
e
p
o
p
u
lar
ch
o
ices.
T
h
is
ap
p
r
o
ac
h
in
v
o
l
v
es
th
e
im
p
lem
e
n
tatio
n
o
f
a
n
en
co
d
er
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ca
p
t
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r
e
c
o
n
tex
t
u
al
f
ea
tu
r
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a
n
d
a
co
r
r
esp
o
n
d
in
g
d
ec
o
d
e
r
to
en
s
u
r
e
s
p
atial
d
etails
ar
e
m
ain
tain
ed
d
u
r
i
n
g
th
e
p
r
o
ce
s
s
,
a
s
d
escr
ib
ed
in
th
e
liter
atu
r
e.
R
ep
r
esen
tativ
e
wo
r
k
an
d
r
atio
n
ale:
UNe
t++
[
2
]
is
an
en
h
an
ce
d
v
er
s
io
n
o
f
th
e
tr
ad
itio
n
al
U
-
Net
ar
ch
itectu
r
e,
in
wh
ich
n
ested
an
d
d
en
s
e
s
k
ip
co
n
n
ec
tio
n
s
ar
e
in
co
r
p
o
r
ated
to
im
p
r
o
v
e
f
ea
t
u
r
e
ag
g
r
eg
atio
n
an
d
g
r
ad
ien
t
f
lo
w.
T
h
is
ap
p
r
o
ac
h
h
as
s
h
o
wn
p
r
o
m
is
in
g
r
esu
lts
in
liv
er
tu
m
o
r
s
eg
m
en
tatio
n
u
s
in
g
MRI
im
ag
es.
Ad
d
itio
n
ally
,
th
e
u
n
if
ied
C
NN
f
r
am
ewo
r
k
d
escr
ib
ed
in
[
3
]
attem
p
ts
to
u
n
if
y
b
o
th
class
if
icat
io
n
an
d
s
eg
m
en
tatio
n
in
to
a
s
in
g
le
f
r
am
ewo
r
k
to
f
ac
ilit
ate
d
iag
n
o
s
tic
p
r
o
ce
s
s
es.
C
NN
-
b
ase
d
ar
ch
itectu
r
es
ar
e
r
ec
o
g
n
ized
f
o
r
th
ei
r
co
m
p
u
t
atio
n
al
ef
f
icien
c
y
an
d
r
elativ
e
s
im
p
licity
in
co
m
p
ar
is
o
n
to
o
th
er
m
o
d
els.
Ho
wev
er
,
th
e
lo
ca
lized
n
atu
r
e
o
f
co
n
v
o
lu
tio
n
o
p
er
atio
n
s
in
C
NNs
p
r
ev
en
ts
th
em
f
r
o
m
ca
p
tu
r
in
g
s
p
atial
r
elatio
n
s
h
ip
s
b
etwe
en
f
ea
tu
r
e
s
,
wh
ich
m
a
y
b
e
ess
en
tial
i
n
tu
m
o
r
s
eg
m
en
tatio
n
,
p
ar
tic
u
lar
ly
in
s
ce
n
ar
io
s
in
v
o
lv
in
g
s
p
atial
co
n
tex
t.
3
.
2
.
At
t
ent
io
n
-
enha
nced
CNN
m
o
dels
I
n
o
r
d
er
t
o
o
v
er
co
m
e
th
e
lim
itatio
n
o
f
C
NNs
in
co
n
tex
tu
a
l
m
o
d
elin
g
,
atten
tio
n
m
ec
h
a
n
is
m
s
h
av
e
b
ee
n
in
c
o
r
p
o
r
ated
to
allo
w
th
e
n
etwo
r
k
to
s
elec
tiv
ely
em
p
h
asize
ce
r
tain
s
p
atial
lo
ca
tio
n
s
o
r
f
ea
t
u
r
e
ch
an
n
els.
T
h
e
atten
tio
n
m
ec
h
an
is
m
s
ca
n
b
e
s
p
atial,
c
h
an
n
el
-
wis
e,
o
r
a
co
m
b
in
atio
n
o
f
b
o
th
.
R
ep
r
e
s
en
tativ
e
wo
r
k
s
a
n
d
r
atio
n
ale:
T
h
e
wo
r
k
o
n
MA
PF
UNe
t
[
4
]
u
tili
ze
s
m
u
lti
-
atten
tio
n
p
er
ce
p
tio
n
m
o
d
u
l
es
with
in
a
C
NN
ar
ch
itectu
r
e
to
en
h
a
n
ce
th
e
s
en
s
itiv
ity
o
f
th
e
n
etwo
r
k
to
s
m
all
an
d
lo
w
co
n
tr
ast
tu
m
o
r
s
with
in
a
C
T
im
ag
in
g
en
v
ir
o
n
m
en
t.
T
h
e
wo
r
k
o
n
L
A
-
R
esUNet
[
5
]
u
tili
ze
s
at
ten
tio
n
g
ates
with
in
a
r
esid
u
al
U
-
Net
ar
ch
itectu
r
e
to
allo
w
th
e
n
etwo
r
k
to
s
elec
tiv
ely
em
p
h
asize
ce
r
tain
lo
ca
tio
n
s
with
in
a
lo
n
g
itu
d
in
al
im
ag
in
g
en
v
ir
o
n
m
e
n
t.
T
h
e
u
s
e
o
f
C
NNs
with
atten
tio
n
m
ec
h
an
is
m
s
h
as
s
h
o
wn
p
r
o
m
is
in
g
r
esu
lts
f
o
r
im
p
r
o
v
in
g
t
h
e
ac
cu
r
ac
y
o
f
th
e
n
etwo
r
k
with
in
ch
allen
g
in
g
en
v
ir
o
n
m
en
ts
;
h
o
wev
er
,
th
e
ad
d
itio
n
o
f
th
ese
m
o
d
u
les
to
th
e
n
etwo
r
k
ca
n
in
cr
ea
s
e
th
e
co
m
p
le
x
ity
o
f
th
e
n
etwo
r
k
a
n
d
,
c
o
n
s
eq
u
en
tl
y
,
th
e
r
is
k
o
f
o
v
er
f
itti
n
g
.
3
.
3
.
T
ra
ns
f
o
rm
er
-
o
nly
m
o
d
els
Ho
w
ev
er
,
i
n
p
u
r
el
y
tr
a
n
s
f
o
r
m
er
m
o
d
els,
as
s
e
en
in
im
p
l
em
en
t
ati
o
n
s
i
n
s
p
ir
e
d
b
y
v
is
i
o
n
t
r
an
s
f
o
r
m
e
r
(
Vi
T
)
,
co
n
v
o
l
u
ti
o
n
al
o
p
e
r
a
ti
o
n
s
ar
e
c
o
m
p
let
el
y
c
ir
cu
m
v
e
n
t
ed
.
Se
lf
-
a
tte
n
ti
o
n
m
e
ch
a
n
is
m
s
a
r
e
s
ee
n
as
t
h
e
m
ai
n
way
o
f
in
te
r
a
cti
o
n
b
et
wee
n
im
ag
e
p
at
c
h
es
.
F
o
r
d
e
n
s
e
p
r
e
d
i
ct
io
n
t
ask
s
s
u
c
h
as
li
v
e
r
s
e
g
m
en
tati
o
n
,
m
o
d
els
s
u
c
h
as
Sw
in
-
UN
et
m
ak
e
u
s
e
o
f
h
i
e
r
a
r
c
h
i
ca
l
s
h
i
f
te
d
wi
n
d
o
w
a
tte
n
tio
n
m
ec
h
a
n
is
m
s
.
K
ey
w
o
r
k
a
n
d
r
at
io
n
ale
:
P
u
r
el
y
tr
a
n
s
f
o
r
m
e
r
m
o
d
els
h
a
v
e
s
h
o
w
n
r
e
m
a
r
k
a
b
le
p
o
t
en
tia
l
i
n
g
l
o
b
al
c
o
n
te
x
t
u
n
d
e
r
s
ta
n
d
i
n
g
.
Ho
we
v
e
r
,
t
h
ei
r
ap
p
li
ca
ti
o
n
i
n
l
iv
e
r
t
u
m
o
r
s
eg
m
en
tatio
n
m
o
d
els
o
f
te
n
r
em
ai
n
s
h
i
n
d
e
r
e
d
d
u
e
t
o
t
h
e
r
e
q
u
i
r
e
m
e
n
t
o
f
la
r
g
e
d
a
tas
ets
as
w
ell
as
h
i
g
h
c
o
m
p
u
ta
ti
o
n
al
ex
p
e
n
s
es.
As
s
e
en
i
n
r
ec
e
n
t
lit
er
at
u
r
e,
it
h
as
b
e
en
s
h
o
w
n
th
at
s
u
c
h
m
o
d
els
o
f
te
n
r
e
m
ai
n
p
l
a
g
u
e
d
b
y
c
h
a
lle
n
g
es
in
lo
ca
l
f
in
e
-
g
r
a
in
e
d
d
et
ails
r
e
q
u
ir
e
d
t
o
u
n
d
e
r
s
ta
n
d
t
u
m
o
r
b
o
u
n
d
ar
y
d
ef
i
n
iti
o
n
s
,
a
p
r
o
b
l
em
t
h
at
h
y
b
r
i
d
m
o
d
els
h
o
p
e
to
o
v
er
c
o
m
e.
3
.
4
.
H
y
brid CNN
–
t
ra
ns
f
o
r
m
er
mo
dels
Hy
b
r
id
ar
ch
itectu
r
es
u
tili
ze
th
e
in
d
u
ctiv
e
b
iases
o
f
C
NN
s
f
o
r
lo
ca
l
f
ea
tu
r
e
ex
tr
ac
tio
n
an
d
t
h
e
ab
ilit
y
o
f
th
e
tr
an
s
f
o
r
m
er
to
ad
d
r
ess
g
lo
b
al
c
o
n
tex
tu
al
d
e
p
en
d
e
n
cies.
T
h
is
is
esp
ec
ially
r
elev
an
t
f
o
r
liv
er
tu
m
o
r
s
th
a
t
o
f
ten
d
is
p
lay
ir
r
eg
u
lar
s
h
ap
es
an
d
lo
w
co
n
tr
ast
with
ad
jace
n
t
tis
s
u
es.
R
ep
r
esen
tativ
e
W
o
r
k
s
:
R
esTr
an
s
UNe
t
[
6
]
co
m
b
in
es
th
e
u
s
e
o
f
r
esid
u
als
with
g
lo
b
al
s
elf
-
atten
tio
n
f
o
r
th
e
p
r
eser
v
atio
n
o
f
s
p
atia
l
r
eso
lu
tio
n
an
d
th
e
ac
q
u
is
itio
n
o
f
wid
e
-
r
a
n
g
e
c
o
n
tex
t
in
f
o
r
m
atio
n
.
MM
E
FU
-
Net
[
7
]
also
e
m
p
lo
y
s
th
e
u
s
e
o
f
m
u
lti
-
en
co
d
er
f
u
s
io
n
f
o
r
f
ea
tu
r
e
r
ep
r
esen
tati
o
n
.
C
r
itical
an
aly
s
is
: c
o
m
p
ar
e
d
with
C
NN
-
b
ased
ar
ch
itectu
r
es th
at
d
o
n
o
t
u
tili
ze
g
lo
b
al
c
o
n
tex
t
in
f
o
r
m
atio
n
an
d
tr
an
s
f
o
r
m
er
-
b
ased
a
r
ch
itectu
r
es
th
at
m
a
y
s
ac
r
if
ice
p
r
ec
is
e
i
n
f
o
r
m
atio
n
l
o
ca
lly
,
th
e
u
s
e
o
f
h
y
b
r
id
ar
ch
itectu
r
e
s
p
r
o
v
id
es
a
well
-
r
o
u
n
d
ed
m
e
th
o
d
o
lo
g
y
.
T
h
e
cu
r
r
e
n
t
r
ev
ie
w
aim
s
to
co
v
er
th
e
p
er
io
d
f
r
o
m
2
0
2
3
-
2
0
2
5
,
d
u
r
in
g
wh
ich
n
ewe
r
ar
c
h
itectu
r
es
h
av
e
p
r
o
p
o
s
ed
th
e
c
o
n
ce
p
t
o
f
“lig
h
tweig
h
t
h
y
b
r
id
izatio
n
”
f
o
r
th
e
s
ig
n
if
ic
an
t c
o
m
p
u
tatio
n
al
co
s
t a
s
s
o
ciate
d
with
th
e
u
s
e
o
f
th
is
m
eth
o
d
o
lo
g
y
.
3
.
5
.
M
ult
i
m
o
da
l
a
nd
ens
e
m
b
le
met
h
o
d
o
l
o
g
ies
Mu
l
tim
o
d
a
l
o
r
e
n
s
e
m
b
le
m
e
th
o
d
o
lo
g
i
es
u
s
e
th
e
b
e
n
e
f
its
o
f
d
if
f
e
r
e
n
t
in
f
o
r
m
a
ti
o
n
s
o
u
r
c
es
p
r
o
v
i
d
e
d
b
y
d
i
f
f
er
e
n
t
im
ag
in
g
m
o
d
al
iti
es
o
r
d
i
f
f
e
r
e
n
t
m
o
d
els
t
o
i
n
cr
ea
s
e
t
h
e
r
o
b
u
s
t
n
ess
o
f
t
h
e
m
o
d
el.
T
h
es
e
m
et
h
o
d
o
lo
g
i
es
ar
e
s
i
m
il
ar
t
o
t
h
e
a
ct
u
a
l
s
c
e
n
a
r
io
,
w
h
e
r
e
i
n
C
T
,
MRI
,
o
r
PE
T
s
ca
n
s
a
r
e
t
ak
e
n
i
n
t
o
c
o
n
s
id
er
at
io
n
s
i
m
u
lt
an
e
o
u
s
ly
.
So
m
e
o
f
t
h
e
r
ep
r
es
e
n
ta
ti
v
e
wo
r
k
s
wit
h
j
u
s
ti
f
i
ca
t
io
n
:
T
h
e
e
n
s
e
m
b
le
m
o
d
e
l
t
h
at
c
o
m
b
i
n
es
R
es
UNe
t
a
n
d
I
n
ce
p
t
io
n
V
4
,
as
d
is
cu
s
s
e
d
i
n
r
e
f
e
r
e
n
c
e
[
8
]
,
s
h
o
ws
h
i
g
h
e
r
r
o
b
u
s
t
n
ess
w
it
h
th
e
c
o
m
b
i
n
a
ti
o
n
o
f
C
T
an
d
MRI
d
at
ase
ts
d
u
e
t
o
t
h
e
u
ti
liz
ati
o
n
o
f
d
if
f
er
en
t
m
o
d
els
.
T
h
e
V
-
N
E
T
-
VG
G1
6
m
o
d
e
l
d
is
c
u
s
s
ed
i
n
r
e
f
e
r
e
n
c
e
[
9
]
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
:
1
3
8
2
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1
3
9
8
1386
co
m
b
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t
h
e
p
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o
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class
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f
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[
1
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co
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f
t
ed
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ad
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o
n
a
l
r
es
o
u
r
ce
s
,
a
n
d
r
eq
u
i
r
e
m
o
r
e
d
at
a
to
b
e
al
ig
n
ed
.
T
h
is
tax
o
n
o
m
y
o
f
f
er
s
a
s
tr
u
ct
u
r
ed
f
r
am
ewo
r
k
t
h
at
m
a
y
b
e
u
s
ed
to
ca
teg
o
r
ize
a
n
d
co
m
p
ar
e
m
o
r
e
th
an
3
8
co
n
tem
p
o
r
ar
y
m
o
d
els
th
at
ar
e
p
r
esen
ted
th
r
o
u
g
h
o
u
t
th
e
co
m
p
ar
ativ
e
s
ec
tio
n
s
th
at
f
o
llo
w.
T
h
e
tax
o
n
o
m
y
r
ep
r
esen
ts
th
e
o
v
er
all
tr
en
d
with
in
th
e
f
ield
,
m
o
v
in
g
f
r
o
m
lo
ca
lized
f
ea
tu
r
e
lear
n
in
g
-
b
ased
m
o
d
els
to
war
d
m
o
r
e
u
n
if
y
in
g
m
o
d
els
th
at
aim
to
in
teg
r
ate
lo
ca
l
an
d
g
lo
b
al
in
f
o
r
m
atio
n
.
T
h
is
tr
en
d
r
e
p
r
esen
ts
th
e
o
v
er
all
tr
en
d
to
war
d
im
p
r
o
v
in
g
r
o
b
u
s
tn
ess
an
d
s
eg
m
en
tatio
n
ac
c
u
r
ac
y
,
as
well
as
s
ev
er
al
p
r
ac
t
ical
co
n
s
id
er
atio
n
s
th
at
ar
e
im
p
o
r
tan
t
with
r
eg
ar
d
to
clin
ical
ap
p
licab
ilit
y
.
Fig
u
r
e
1
p
r
esen
ts
a
s
tr
u
ctu
r
ed
tax
o
n
o
m
y
o
f
s
o
m
e
o
f
th
e
m
o
r
e
p
r
o
m
in
e
n
t
d
ee
p
lea
r
n
in
g
m
o
d
els
th
at
ar
e
cu
r
r
e
n
tly
em
p
lo
y
ed
i
n
th
e
co
n
te
x
t
o
f
m
ed
ical
im
ag
e
s
eg
m
en
tatio
n
,
with
a
f
o
cu
s
o
n
th
e
an
aly
s
is
o
f
im
ag
es
o
f
th
e
liv
er
a
n
d
liv
er
t
u
m
o
r
s
.
T
h
e
tax
o
n
o
m
y
p
r
esen
ts
th
e
p
r
ev
ailin
g
a
p
p
r
o
ac
h
es
with
in
th
e
f
ield
as
o
n
e
o
f
f
o
u
r
m
a
in
ar
ch
itectu
r
al
p
ar
ad
i
g
m
s
:
co
n
v
o
lu
ti
o
n
al
n
e
u
r
al
n
etwo
r
k
,
h
y
b
r
i
d
C
NN
-
tr
an
s
f
o
r
m
er
,
tr
an
s
f
o
r
m
er
,
a
n
d
m
u
ltimo
d
al
en
s
em
b
le
m
o
d
els.
T
ab
le
1
p
r
o
v
id
es
a
s
tr
u
ctu
r
ed
co
m
p
ar
ativ
e
o
v
er
v
iew
o
f
f
i
v
e
wid
ely
ad
o
p
te
d
ar
ch
itectu
r
al
p
ar
ad
ig
m
s
in
d
ee
p
lear
n
in
g
-
b
ased
liv
er
an
d
tu
m
o
r
s
eg
m
en
tatio
n
.
I
t
s
u
m
m
ar
izes
th
eir
co
r
e
d
esig
n
p
r
in
cip
les,
r
ep
r
esen
tativ
e
im
p
lem
e
n
tatio
n
s
,
co
m
m
o
n
ly
r
ep
o
r
te
d
s
tr
en
g
th
s
,
an
d
ty
p
ical
lim
itatio
n
s
as
d
is
cu
s
s
ed
in
th
e
liter
atu
r
e.
T
h
e
co
m
p
a
r
is
o
n
aim
s
to
s
u
p
p
o
r
t
q
u
alitativ
e
m
o
d
el
u
n
d
e
r
s
tan
d
in
g
an
d
h
ig
h
lig
h
t
g
en
e
r
al
ar
ch
itectu
r
al
tr
en
d
s
r
ath
er
th
a
n
s
tan
d
ar
d
ized
p
er
f
o
r
m
an
ce
b
e
n
ch
m
ar
k
i
n
g
.
T
ab
le
1
.
C
o
m
p
a
r
ativ
e
an
aly
s
is
o
f
d
ee
p
lear
n
in
g
m
o
d
el
ar
ch
et
y
p
es f
o
r
liv
e
r
s
eg
m
en
tatio
n
C
a
t
e
g
o
r
y
C
o
r
e
D
e
scr
i
p
t
i
o
n
Ex
e
m
p
l
a
r
y
S
t
u
d
i
e
s
P
r
i
n
c
i
p
a
l
S
t
r
e
n
g
t
h
s
K
e
y
Li
m
i
t
a
t
i
o
n
s
CNN
-
b
a
sed
mo
d
e
ls
U
t
i
l
i
z
e
e
n
c
o
d
e
r
-
d
e
c
o
d
e
r
a
r
c
h
i
t
e
c
t
u
r
e
s
w
i
t
h
c
o
n
v
o
l
u
t
i
o
n
a
l
l
a
y
e
r
s fo
r
h
i
e
r
a
r
c
h
i
c
a
l
l
o
c
a
l
f
e
a
t
u
r
e
e
x
t
r
a
c
t
i
o
n
.
[
2
]
,
[
3
]
C
o
mpu
t
a
t
i
o
n
a
l
e
f
f
i
c
i
e
n
c
y
,
p
r
o
v
e
n
r
e
l
i
a
b
i
l
i
t
y
,
e
a
s
y
p
i
p
e
l
i
n
e
i
n
t
e
g
r
a
t
i
o
n
.
L
i
m
i
t
e
d
r
e
c
e
p
t
i
v
e
f
i
e
l
d
h
a
m
p
e
r
s
l
o
n
g
-
r
a
n
g
e
d
e
p
e
n
d
e
n
c
y
mo
d
e
l
i
n
g
;
st
r
u
g
g
l
e
s
w
i
t
h
i
r
r
e
g
u
l
a
r
t
u
mo
r
b
o
u
n
d
a
r
i
e
s
.
A
t
t
e
n
t
i
o
n
-
b
a
sed
mo
d
e
l
s
I
n
t
e
g
r
a
t
e
a
t
t
e
n
t
i
o
n
mo
d
u
l
e
s
i
n
t
o
C
N
N
s t
o
d
y
n
a
mi
c
a
l
l
y
f
o
c
u
s
o
n
sal
i
e
n
t
r
e
g
i
o
n
s
l
i
k
e
t
u
m
o
r
m
a
r
g
i
n
s
.
[
1
]
,
[
4
]
,
[
5
]
En
h
a
n
c
e
d
b
o
u
n
d
a
r
y
d
e
l
i
n
e
a
t
i
o
n
,
b
e
t
t
e
r
h
a
n
d
l
i
n
g
o
f
h
e
t
e
r
o
g
e
n
e
o
u
s
t
e
x
t
u
r
e
s
a
n
d
s
mal
l
l
e
s
i
o
n
s.
I
n
t
r
o
d
u
c
e
s
m
o
d
e
r
a
t
e
c
o
mpu
t
a
t
i
o
n
a
l
o
v
e
r
h
e
a
d
;
r
e
q
u
i
r
e
s
c
a
r
e
f
u
l
a
r
c
h
i
t
e
c
t
u
r
a
l
d
e
si
g
n
.
Hy
b
r
i
d
C
N
N
–
t
r
a
n
sf
o
r
m
e
r
mo
d
e
l
s
F
u
se
C
N
N
e
n
c
o
d
e
r
s
(
l
o
c
a
l
f
e
a
t
u
r
e
s)
w
i
t
h
t
r
a
n
sf
o
r
mer
m
o
d
u
l
e
s
(
g
l
o
b
a
l
c
o
n
t
e
x
t
)
f
o
r
c
o
m
p
l
e
me
n
t
a
r
y
mo
d
e
l
i
n
g
.
[
6
]
,
[
7
]
,
[
1
1
]
,
S
u
p
e
r
i
o
r
a
c
c
u
r
a
c
y
;
c
a
p
t
u
r
e
s
b
o
t
h
l
o
c
a
l
d
e
t
a
i
l
s a
n
d
g
l
o
b
a
l
a
n
a
t
o
mi
c
a
l
c
o
n
t
e
x
t
;
st
a
t
e
-
of
-
t
h
e
-
a
r
t
p
e
r
f
o
r
m
a
n
c
e
.
Hi
g
h
c
o
mpu
t
a
t
i
o
n
a
l
/
m
e
m
o
r
y
d
e
ma
n
d
s
;
c
o
m
p
l
e
x
t
r
a
i
n
i
n
g
;
l
o
n
g
e
r
i
n
f
e
r
e
n
c
e
t
i
m
e
s.
T
r
a
n
sf
o
r
me
r
-
o
n
l
y
mo
d
e
l
s
R
e
l
y
e
x
c
l
u
s
i
v
e
l
y
o
n
sel
f
-
a
t
t
e
n
t
i
o
n
t
o
m
o
d
e
l
g
l
o
b
a
l
d
e
p
e
n
d
e
n
c
i
e
s
w
i
t
h
o
u
t
c
o
n
v
o
l
u
t
i
o
n
a
l
b
i
a
s.
[
1
1
]
E
x
c
e
p
t
i
o
n
a
l
g
l
o
b
a
l
c
o
n
t
e
x
t
i
n
t
e
g
r
a
t
i
o
n
;
p
r
o
m
i
si
n
g
f
o
u
n
d
a
t
i
o
n
a
l
a
c
c
u
r
a
c
y
.
E
x
t
r
e
m
e
l
y
d
a
t
a
-
h
u
n
g
r
y
;
h
i
g
h
e
s
t
c
o
m
p
u
t
a
t
i
o
n
a
l
c
o
s
t
;
l
e
ss
e
x
p
l
o
r
e
d
f
o
r
3
D
me
d
i
c
a
l
se
g
me
n
t
a
t
i
o
n
.
M
u
l
t
i
-
m
o
d
a
l
a
n
d
e
n
s
e
mbl
e
mo
d
e
l
s
C
o
m
b
i
n
e
m
u
l
t
i
p
l
e
i
ma
g
i
n
g
m
o
d
a
l
i
t
i
e
s
o
r
a
g
g
r
e
g
a
t
e
p
r
e
d
i
c
t
i
o
n
s
f
r
o
m se
v
e
r
a
l
m
o
d
e
l
s t
o
i
mp
r
o
v
e
r
o
b
u
st
n
e
ss.
[
9
]
,
[
1
2
]
,
[
1
3
]
I
n
c
r
e
a
se
d
r
o
b
u
st
n
e
ss
/
g
e
n
e
r
a
l
i
z
a
t
i
o
n
;
l
e
v
e
r
a
g
e
s
c
o
mp
l
e
me
n
t
a
r
y
i
n
f
o
r
mat
i
o
n
f
r
o
m
d
i
f
f
e
r
e
n
t
s
o
u
r
c
e
s.
H
i
g
h
s
y
st
e
m
c
o
m
p
l
e
x
i
t
y
;
d
e
p
e
n
d
e
n
t
on
co
-
r
e
g
i
st
e
r
e
d
m
u
l
t
i
-
m
o
d
a
l
d
a
t
a
;
r
e
so
u
r
c
e
-
i
n
t
e
n
si
v
e
t
r
a
i
n
i
n
g
.
4.
M
E
T
H
O
DO
L
O
G
Y
F
O
R
L
I
T
E
R
AT
U
RE
S
E
ARCH
AN
D
ANALY
SI
S
T
h
is
s
y
s
tem
atic
r
ev
iew
f
o
llo
ws
th
e
p
r
ef
e
r
r
ed
r
ep
o
r
tin
g
it
em
s
f
o
r
s
y
s
tem
atic
r
ev
iews
an
d
m
eta
-
an
aly
s
es
(
PR
I
SM
A
2
0
2
0
)
g
u
i
d
elin
es.
T
h
e
s
ea
r
ch
was
co
n
d
u
cted
ac
r
o
s
s
f
o
u
r
m
ajo
r
d
atab
ases
:
I
E
E
E
Xp
lo
r
e,
Pu
b
Me
d
,
Scien
ce
Dir
ec
t,
an
d
Go
o
g
le
Sch
o
lar
.
T
h
e
s
ea
r
ch
s
tr
in
g
s
u
s
ed
wer
e:
(
'
Hy
b
r
id
C
NN'
OR
'
T
r
an
s
f
o
r
m
er
'
)
AND
(
'
L
iv
er
t
u
m
o
r
'
OR
'
Hep
ato
ce
llu
lar
C
a
r
cin
o
m
a'
)
AND
'
Seg
m
en
tatio
n
'
.
T
h
e
f
in
al
s
ea
r
ch
was u
p
d
ated
o
n
[
wr
ite
d
ate
,
J
a
n
u
ar
y
1
5
,
2
0
2
6
]
.
4
.
1
.
Sea
rc
h St
ra
t
eg
y
A
c
o
m
p
r
e
h
e
n
s
i
v
e
li
te
r
at
u
r
e
s
ea
r
c
h
was
p
e
r
f
o
r
m
e
d
i
n
th
e
f
o
ll
o
wi
n
g
e
le
ctr
o
n
i
c
d
at
a
b
ases
:
Pu
b
Me
d
/
ME
D
L
I
NE
,
I
E
E
E
X
p
l
o
r
e,
Sc
o
p
u
s
,
W
e
b
o
f
Sc
ie
n
ce
,
S
p
r
in
g
er
L
i
n
k
,
a
n
d
t
h
e
A
C
M
Di
g
it
al
L
i
b
r
a
r
y
.
Ad
d
it
io
n
a
ll
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I
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4
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3
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Study
s
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t
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s
s
T
h
e
s
tu
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y
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p
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s
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as
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s
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f
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r
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in
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tly
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with
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0
0
r
ec
o
r
d
s
.
T
h
e
r
em
ain
in
g
1
2
0
f
u
ll
-
tex
t
a
r
ticles
wer
e
ass
es
s
ed
f
o
r
elig
ib
ilit
y
,
o
f
wh
ich
5
7
wer
e
ex
clu
d
e
d
f
o
r
r
ea
s
o
n
s
s
u
ch
as
u
n
s
u
itab
le
s
t
u
d
y
d
esig
n
,
p
o
p
u
latio
n
,
o
r
f
ailu
r
e
to
m
ee
t
in
clu
s
io
n
cr
iter
ia.
C
o
n
s
eq
u
en
tly
,
3
8
s
tu
d
ies
wer
e
in
clu
d
e
d
in
th
e
f
in
al
q
u
alitativ
e
an
d
q
u
a
n
titativ
e
s
y
n
th
esis
.
No
ad
d
itio
n
al
r
ec
o
r
d
s
wer
e
id
en
tifie
d
f
r
o
m
o
th
e
r
s
o
u
r
ce
s
.
4
.
4
.
Da
t
a
ex
t
r
a
ct
io
n a
nd
s
y
nthesis
Fo
r
ea
ch
of
th
e
s
elec
ted
s
tu
d
ie
s
,
th
e
f
o
llo
win
g
d
ata
wer
e
ex
tr
ac
ted
in
to
a
s
tan
d
ar
d
ized
tem
p
late:
a.
B
ib
lio
m
etr
ic
in
f
o
r
m
atio
n
:
a
u
th
o
r
s
,
y
ea
r
,
j
o
u
r
n
al/c
o
n
f
er
e
n
ce
.
b.
Mo
d
el
ar
ch
itectu
r
e:
m
o
d
e
l
n
a
m
e
,
a
r
c
h
itec
tu
r
a
l
f
am
ily
(
e
.
g
.
,
C
NN
-
b
ased
,
atten
tio
n
-
en
h
an
ce
d
,
h
y
b
r
id
C
NN
-
tran
s
f
o
r
m
er
,
tra
n
s
f
o
r
m
e
r
-
o
n
ly
,
m
u
lt
imo
d
al/en
s
em
b
l
e)
,
an
d
co
r
e
tech
n
ical
co
m
p
o
n
e
n
ts
(
e.
g
.
,
atten
tio
n
g
ates,
tr
an
s
f
o
r
m
er
b
l
o
ck
s
,
f
u
s
io
n
s
tr
ateg
ies).
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
:
1
3
8
2
-
1
3
9
8
1388
c.
E
x
p
er
im
en
tal
s
etu
p
:
b
e
n
ch
m
a
r
k
d
ataset
(
s
)
em
p
lo
y
e
d
(
e.
g
.
,
L
iTS,
3
DI
R
C
ADb
,
C
HAO
S),
d
ata
s
p
lits
,
p
r
ep
r
o
ce
s
s
in
g
d
etails,
an
d
v
ali
d
atio
n
s
tr
ateg
y
(
e
.
g
.
,
c
r
o
s
s
-
v
al
id
atio
n
,
f
ix
e
d
s
p
lit).
d.
Per
f
o
r
m
an
ce
m
et
r
ics:
r
ep
o
r
te
d
q
u
an
titativ
e
r
esu
lts
,
s
p
ec
if
ically
Dice
s
co
r
es
f
o
r
liv
er
an
d
tu
m
o
r
s
eg
m
en
tatio
n
,
in
ter
s
ec
tio
n
o
v
e
r
u
n
io
n
(
I
o
U)
,
an
d
Hau
s
d
o
r
f
f
d
is
tan
ce
(
HD9
5
)
wh
er
e
av
aila
b
le.
e.
C
o
m
p
u
tatio
n
al
p
r
o
f
ile:
r
ep
o
r
te
d
in
f
o
r
m
atio
n
o
n
m
o
d
el
co
m
p
l
ex
ity
(
n
u
m
b
er
o
f
p
a
r
am
eter
s
)
,
tr
ain
in
g
r
eq
u
ir
em
e
n
ts
,
an
d
in
f
er
en
ce
s
p
ee
d
,
if
av
ailab
le.
f.
Key
f
in
d
in
g
s
an
d
lim
itatio
n
s
:
s
tr
en
g
th
s
o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
,
claim
ed
clin
ical
r
elev
an
ce
,
an
d
lim
itatio
n
s
n
o
ted
b
y
t
h
e
au
th
o
r
s
.
Fig
u
r
e
2
.
PR
I
SMA
2
0
2
0
f
lo
w
d
iag
r
am
o
f
th
e
s
tu
d
y
s
elec
tio
n
p
r
o
ce
s
s
4
.
5
.
Ana
ly
t
ica
l
a
pp
ro
a
ch
T
h
e
ex
tr
ac
te
d
d
ata
wer
e
a
n
aly
ze
d
q
u
alitativ
ely
alo
n
g
f
o
u
r
p
r
im
ar
y
d
i
m
en
s
io
n
s
,
alig
n
in
g
with
th
e
tax
o
n
o
m
y
p
r
o
p
o
s
ed
i
n
Sectio
n
3
:
a.
Ar
ch
itectu
r
al
d
esig
n
an
d
in
n
o
v
atio
n
:
ass
ess
in
g
th
e
n
o
v
elty
o
f
ar
ch
itectu
r
al
in
te
g
r
atio
n
s
tr
at
eg
ies.
b.
R
ep
o
r
ted
s
eg
m
en
tatio
n
p
er
f
o
r
m
an
ce
:
a
co
m
p
a
r
ativ
e
d
is
cu
s
s
io
n
o
f
r
esu
lts
as
p
r
esen
ted
in
th
e
o
r
ig
in
al
s
tu
d
ies,
with
ca
r
ef
u
l c
o
n
s
id
er
a
tio
n
o
f
th
e
h
eter
o
g
en
eity
in
ex
p
er
im
en
tal
s
ettin
g
s
.
c.
C
o
m
p
u
tatio
n
al
ef
f
icien
cy
a
n
d
co
m
p
lex
ity
:
an
aly
zi
n
g
th
e
tr
ad
e
-
o
f
f
b
etwe
en
ac
cu
r
ac
y
an
d
r
eso
u
r
ce
r
eq
u
ir
em
e
n
ts
.
d.
C
lin
ical
tr
an
s
latio
n
p
o
ten
tial:
ev
alu
atin
g
asp
ec
ts
s
u
ch
as
r
o
b
u
s
tn
ess
,
g
en
er
aliza
b
ilit
y
,
an
d
alig
n
m
en
t
with
clin
ical
wo
r
k
f
lo
ws.
“
N
o
n
ew ex
p
erimen
ts
w
ere
co
n
d
u
cted
;
a
ll res
u
lts
a
r
e
r
ep
o
r
t
ed
a
s
s
ta
ted
in
th
e
o
r
ig
in
a
l p
u
b
lica
tio
n
s
.
”
5.
RE
L
AT
E
D
WO
RK
Pre
cise
an
d
au
to
m
atic
m
ed
i
ca
l
im
ag
e
s
eg
m
en
tatio
n
h
as
tr
ad
itio
n
ally
b
ee
n
at
th
e
e
p
icen
ter
o
f
co
m
p
u
tatio
n
al
h
ep
at
o
lo
g
y
,
f
u
eled
b
y
th
e
m
ed
ical
n
ee
d
f
o
r
th
e
ea
r
ly
d
etec
tio
n
an
d
d
iag
n
o
s
is
o
f
liv
er
ca
n
ce
r
.
C
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
h
av
e
laid
th
e
f
o
u
n
d
atio
n
f
o
r
t
h
e
cu
r
r
en
t
er
a
o
f
au
to
m
atic
m
ed
ical
im
ag
e
s
eg
m
en
tatio
n
th
r
o
u
g
h
t
h
e
ab
il
ity
o
f
d
ee
p
lear
n
in
g
m
o
d
els
t
o
lear
n
h
ier
ar
c
h
ical
f
ea
tu
r
es
a
n
d
s
h
o
w
im
p
r
o
v
ed
p
er
f
o
r
m
an
ce
o
v
er
co
n
v
en
tio
n
al
ap
p
r
o
ac
h
es.
T
h
e
ev
o
lu
tio
n
o
f
m
ed
ical
im
ag
e
s
eg
m
e
n
tatio
n
h
as
f
o
ll
o
wed
th
e
tr
ajec
to
r
y
o
f
s
u
cc
ess
iv
e
im
p
r
o
v
em
en
ts
in
ar
c
h
itectu
r
es,
in
clu
d
in
g
th
e
ad
d
itio
n
o
f
atten
t
io
n
m
ec
h
a
n
is
m
s
to
f
o
cu
s
th
e
m
o
d
el'
s
atten
tio
n
o
n
im
p
o
r
tan
t
r
eg
io
n
s
o
f
th
e
i
m
ag
e,
s
u
c
h
as
t
h
e
tu
m
o
r
b
o
u
n
d
ar
ies,
tr
a
n
s
f
o
r
m
er
ar
ch
itectu
r
es
f
o
r
th
e
m
o
d
elin
g
o
f
lo
n
g
-
r
a
n
g
e
d
e
p
en
d
e
n
cie
s
,
an
d
th
e
u
s
e
o
f
h
y
b
r
id
m
o
d
els
co
m
b
in
in
g
t
h
e
ad
v
an
tag
es
o
f
C
NNs
an
d
tr
an
s
f
o
r
m
er
s
.
Pre
v
io
u
s
r
ev
iew
ar
ticles
h
av
e
p
r
esen
ted
a
r
ev
iew
o
f
th
e
latest
d
ev
elo
p
m
e
n
ts
in
m
ed
ical
im
ag
e
s
eg
m
en
tatio
n
,
in
cl
u
d
in
g
th
o
s
e
u
s
ed
in
th
e
co
n
tex
t
o
f
ab
d
o
m
in
al
o
r
liv
er
im
ag
in
g
.
T
h
e
r
ev
iew
ar
ticles
o
f
f
er
im
p
o
r
tan
t
in
s
ig
h
ts
in
to
t
h
e
ar
ch
itectu
r
es,
d
atasets
,
an
d
ev
alu
atio
n
m
etr
ics
u
s
ed
in
th
e
m
o
d
els.
Ho
wev
er
,
th
e
r
ev
iew
o
f
th
e
liter
atu
r
e
also
r
ev
ea
ls
a
lim
itatio
n
in
th
e
f
o
r
m
o
f
th
e
ab
s
en
c
e
o
f
a
co
m
p
ar
ativ
e
d
is
cu
s
s
io
n
o
f
th
e
r
esu
lts
o
f
th
e
m
o
d
els
an
d
th
e
lack
o
f
a
r
e
v
iew
o
f
th
e
latest
h
y
b
r
i
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Hyb
r
id
co
n
vo
lu
tio
n
a
l n
e
u
r
a
l
n
etw
o
r
k
–
tr
a
n
s
fo
r
mer m
o
d
els f
o
r
…
(
I
b
r
a
h
im
Mo
h
a
med
A
ttiy
a
)
1389
ar
ch
itectu
r
es,
in
clu
d
in
g
th
o
s
e
p
r
o
p
o
s
ed
f
r
o
m
2
0
2
3
t
o
2
0
2
5
.
T
h
is
s
u
r
v
e
y
p
r
o
v
i
d
es
an
an
al
y
tical
o
v
er
v
iew
o
f
th
e
ex
is
tin
g
3
8
liv
er
-
r
elate
d
s
tu
d
ies
f
r
o
m
t
h
e
y
ea
r
s
2
0
2
3
-
2
0
2
5
,
as
lis
ted
i
n
T
a
b
le
3
(
ap
p
en
d
ix
)
.
T
h
e
co
m
p
ar
ativ
e
in
s
ig
h
ts
f
r
o
m
th
e
ex
is
tin
g
liter
atu
r
e
will
h
elp
id
en
tify
th
e
o
v
er
all
tr
en
d
o
f
th
e
ev
o
l
u
tio
n
o
f
ar
ch
itectu
r
es
in
th
e
f
ield
o
f
liv
er
s
eg
m
en
tatio
n
task
s
.
T
h
e
h
y
b
r
id
ar
c
h
itectu
r
es
o
f
C
NN
an
d
tr
an
s
f
o
r
m
er
m
o
d
els,
s
u
ch
as
th
e
R
esTr
an
s
UNe
t
[
1
3
]
,
an
d
m
u
ltimo
d
al
ar
ch
itectu
r
es,
s
u
ch
as
th
e
R
esU
Net
-
I
n
ce
p
tio
n
V4
[
8
]
,
h
av
e
s
h
o
wn
g
o
o
d
r
esu
lts
in
a
ch
iev
in
g
h
ig
h
Dice
s
co
r
es
with
in
cr
ea
s
ed
c
o
m
p
u
tatio
n
al
r
e
q
u
ir
em
en
ts
.
On
th
e
o
th
er
h
an
d
,
lig
h
tweig
h
t
ar
c
h
itectu
r
es
o
f
C
NN
m
o
d
els,
s
u
ch
as
th
e
UNe
t7
0
[
1
4
]
an
d
L
iv
er
Net
[
1
5
]
,
a
r
e
im
p
o
r
tan
t
f
o
r
th
e
d
e
p
lo
y
m
e
n
t
o
f
th
e
m
o
d
els
in
a
clin
ical
en
v
ir
o
n
m
en
t,
co
n
s
id
er
in
g
th
e
r
ed
u
ce
d
c
o
m
p
u
tatio
n
al
r
eq
u
ir
em
e
n
ts
.
T
h
e
r
em
o
v
al
o
f
n
o
n
-
liv
er
-
r
elate
d
s
tu
d
ies
wi
ll
h
elp
r
ea
d
er
s
f
o
c
u
s
m
o
r
e
o
n
th
e
c
h
allen
g
es
o
f
liv
er
-
r
elate
d
task
s
an
d
th
e
o
v
e
r
all
r
eq
u
ir
em
e
n
ts
o
f
th
e
m
o
d
el
f
o
r
r
o
b
u
s
t r
esu
lts
.
T
h
is
s
y
n
th
esis
o
f
th
e
ex
is
tin
g
liter
atu
r
e
will
h
elp
r
ea
d
er
s
g
et
a
co
m
p
r
eh
e
n
s
iv
e
o
v
er
v
i
ew
o
f
th
e
o
v
er
all
tr
e
n
d
o
f
th
e
ev
o
lu
tio
n
o
f
th
e
m
o
d
els
a
n
d
th
e
ch
ar
a
cter
is
tics
o
f
th
e
m
o
d
els
u
s
ed
f
o
r
t
h
e
liv
e
r
tu
m
o
r
s
eg
m
en
tatio
n
task
.
T
ab
le
3
p
r
o
v
id
es
a
co
m
p
ar
ativ
e
o
v
er
v
ie
w
o
f
3
8
r
e
p
r
esen
tativ
e
d
ee
p
l
ea
r
n
in
g
m
o
d
els
f
o
r
liv
er
an
d
liv
er
tu
m
o
r
s
eg
m
e
n
t
atio
n
.
T
h
e
r
ep
o
r
ted
Dice
s
co
r
e
s
(
liv
er
/tu
m
o
r
)
an
d
k
ey
s
tr
en
g
t
h
s
an
d
wea
k
n
ess
es
ar
e
s
u
m
m
ar
ized
as
p
r
esen
ted
in
th
e
o
r
ig
in
al
s
tu
d
ies.
I
t
is
im
p
o
r
tan
t
to
n
o
te
th
at
th
ese
r
esu
lts
ar
e
b
ased
o
n
r
ep
o
r
ted
p
e
r
f
o
r
m
an
ce
in
t
h
e
li
ter
atu
r
e
a
n
d
we
r
e
o
b
tain
ed
u
n
d
er
h
eter
o
g
e
n
eo
u
s
ex
p
er
im
en
t
al
co
n
d
itio
n
s
.
T
h
is
o
v
er
v
iew
is
in
ten
d
e
d
to
h
i
g
h
li
g
h
t
g
en
e
r
al
tr
en
d
s
,
ar
ch
itectu
r
al
ch
ar
ac
ter
is
tics
,
an
d
p
r
ac
tica
l
co
n
s
id
er
atio
n
s
f
o
r
m
o
d
el
s
elec
tio
n
,
r
ath
e
r
th
an
to
s
er
v
e
as a
s
tan
d
ar
d
ized
b
en
ch
m
ar
k
in
g
o
f
m
o
d
els.
As
s
h
o
wn
in
T
ab
le
4
,
a
r
ig
o
r
o
u
s
q
u
ality
ass
ess
m
en
t
wa
s
p
er
f
o
r
m
ed
f
o
r
all
3
7
in
cl
u
d
ed
s
tu
d
ies
b
ased
o
n
f
i
v
e
k
ey
c
r
iter
ia.
T
h
e
r
esu
l
ts
in
d
icate
th
at
1
0
0
%
o
f
th
e
s
elec
ted
p
ap
e
r
s
p
r
o
v
id
ed
a
clea
r
d
ef
i
n
itio
n
o
f
t
h
e
s
eg
m
en
tatio
n
task
an
d
u
s
ed
s
tan
d
ar
d
ized
ev
al
u
atio
n
m
etr
ic
s
s
u
ch
as
th
e
d
ice
s
im
ilar
ity
co
ef
f
icien
t
(
DSC
)
.
W
h
ile
th
e
m
ajo
r
ity
o
f
s
tu
d
ies
(
9
2
%)
o
f
f
er
ed
co
m
p
r
eh
e
n
s
iv
e
d
ataset
d
escr
ip
tio
n
s
,
ab
o
u
t
6
3
%
p
r
o
v
i
d
ed
a
n
in
-
d
ep
th
d
is
cu
s
s
io
n
r
eg
ar
d
in
g
c
o
m
p
u
tatio
n
al
co
m
p
lex
ity
an
d
r
eso
u
r
ce
r
eq
u
i
r
em
en
ts
.
Ov
er
all
,
th
e
h
ig
h
a
d
h
er
e
n
ce
to
th
ese
q
u
ality
m
ar
k
er
s
en
s
u
r
es th
e
r
eliab
ilit
y
o
f
th
e
c
o
m
p
ar
ativ
e
an
aly
s
is
p
r
esen
ted
in
th
is
r
ev
iew.
”
T
ab
le
4
.
Qu
ality
ass
ess
m
en
t o
f
in
clu
d
ed
s
tu
d
ies
Q
u
a
l
i
t
y
C
r
i
t
e
r
i
a
Nu
mb
e
r
o
f
St
u
d
i
e
s
(
N=
3
7
)
P
e
r
c
e
n
t
a
g
e
(
%)
Cl
e
ar
o
b
j
e
ct
i
v
e
a
n
d
sc
o
p
e
37
1
0
0
%
C
o
m
p
r
e
h
e
n
si
v
e
d
a
t
a
se
t
d
e
s
c
r
i
p
t
i
o
n
34
9
2
%
C
l
e
a
r
l
y
d
e
f
i
n
e
d
e
v
a
l
u
a
t
i
o
n
m
e
t
r
i
c
s (D
i
c
e
/
I
o
U
)
37
1
0
0
%
S
p
e
c
i
f
i
e
d
v
a
l
i
d
a
t
i
o
n
m
e
t
h
o
d
(
e
.
g
.
,
k
-
f
o
l
d
)
33
8
9
%
D
i
scu
ssi
o
n
o
f
c
o
mp
u
t
a
t
i
o
n
a
l
c
o
mp
l
e
x
i
t
y
23
6
2
%
6.
ANALY
SI
S
O
F
CO
M
P
ARA
T
I
V
E
RE
SU
L
T
S A
ND
E
M
E
RG
I
NG
T
R
E
NDS
T
h
e
o
v
er
v
iew
in
T
a
b
le
3
en
ab
les
an
o
r
g
an
ized
d
is
cu
s
s
io
n
o
f
r
ep
o
r
ted
p
er
f
o
r
m
a
n
ce
tr
en
d
s
,
ar
ch
itectu
r
al
f
ea
tu
r
es,
a
n
d
im
p
lem
en
tatio
n
tr
ad
e
-
o
f
f
s
am
o
n
g
3
8
m
o
d
er
n
m
o
d
els
f
o
r
liv
e
r
an
d
liv
er
tu
m
o
r
s
eg
m
en
tatio
n
.
I
n
s
tead
o
f
co
m
p
ar
in
g
m
o
d
els
in
ter
m
s
o
f
r
an
k
,
th
is
s
ec
tio
n
will
s
y
n
th
esize
liter
atu
r
e
to
p
o
in
t
o
u
t tr
en
d
s
r
ele
v
an
t to
b
o
th
r
esear
ch
an
d
clin
ical
in
ter
ests
.
6
.
1
.
P
er
f
o
r
m
a
nce
–
a
cc
ura
c
y
t
ra
de
-
o
f
f
s
A
co
m
m
o
n
th
em
e
in
all
th
e
p
a
p
er
s
ex
am
in
ed
h
er
e
h
as
b
ee
n
th
is
b
alan
ce
b
etwe
en
h
o
w
well
a
m
eth
o
d
p
er
f
o
r
m
s
in
ter
m
s
o
f
ac
c
u
r
a
cy
,
an
d
h
o
w
c
o
m
p
u
tatio
n
all
y
ex
p
en
s
iv
e
it
is
.
Hy
b
r
id
a
r
ch
itectu
r
es
s
u
ch
as
R
esTr
an
s
UNe
t
[
1
3
]
,
wh
ich
a
r
e
a
c
o
m
b
in
atio
n
o
f
C
NNs
a
n
d
tr
a
n
s
f
o
r
m
er
s
,
an
d
en
s
em
b
le
m
eth
o
d
s
s
u
ch
as
R
esUNe
t
-
I
n
ce
p
tio
n
V4
[
8
]
,
wh
ich
ar
e
c
o
m
b
in
atio
n
s
o
f
two
o
r
m
o
r
e
ar
c
h
itectu
r
es,
ac
h
iev
e
h
ig
h
v
alu
es
o
f
th
e
Dice
s
co
r
e.
Ho
wev
er
,
th
ey
ar
e
co
m
p
u
tatio
n
ally
ex
p
en
s
iv
e,
r
eq
u
ir
in
g
a
lo
t
o
f
m
em
o
r
y
o
n
t
h
e
GPU
an
d
l
o
n
g
er
tr
ain
in
g
tim
es.
C
o
n
v
er
s
ely
,
s
m
aller
C
NNs
s
u
ch
as
UNe
t7
0
[
1
4
]
a
n
d
L
iv
er
Net
[
1
5
]
,
wh
ich
ar
e
d
esig
n
ed
f
o
r
f
aster
in
f
er
en
ce
an
d
lo
wer
r
e
s
o
u
r
ce
u
s
ag
e,
ar
e
im
p
o
r
tan
t
f
o
r
p
r
ac
tical
d
ep
l
o
y
m
en
t,
esp
e
cially
in
s
itu
atio
n
s
wh
er
e
tim
e
is
o
f
th
e
ess
en
ce
,
s
u
ch
as in
a
clin
ical
s
ettin
g
.
6
.
2
.
Arc
hite
ct
ura
l
e
v
o
lutio
n
a
nd
inn
o
v
a
t
io
n
T
h
e
tax
o
n
o
m
y
a
n
d
c
o
m
p
ar
ativ
e
o
b
s
er
v
atio
n
s
r
e
v
ea
l a
clea
r
a
r
ch
itectu
r
al
p
r
o
g
r
ess
io
n
:
a.
Fro
m
th
e
lo
ca
l
d
etails
to
th
e
g
lo
b
al
co
n
tex
t:
th
e
f
ield
is
m
o
v
in
g
b
ey
o
n
d
p
lain
C
NNs
an
d
i
n
to
th
e
r
ea
lm
o
f
atten
tio
n
-
r
ich
an
d
h
y
b
r
id
ar
c
h
itectu
r
es.
T
h
e
tar
g
et
is
to
e
x
ten
d
th
e
lim
ited
v
iew
o
f
th
e
co
n
v
o
lu
tio
n
al
f
ilter
,
esp
ec
ially
in
th
e
p
r
esen
ce
o
f
t
u
m
o
r
s
with
ir
r
eg
u
lar
s
h
ap
es a
n
d
v
ar
ie
d
ap
p
ea
r
an
ce
s
.
b.
Am
o
n
g
th
e
n
ew
tr
e
n
d
s
,
th
e
u
s
e
o
f
Hy
b
r
id
Ar
c
h
itectu
r
e
s
is
p
r
o
m
in
en
t,
esp
ec
ially
C
NN
-
tr
an
s
f
o
r
m
er
h
y
b
r
id
s
,
wh
ich
a
r
e
g
ain
in
g
m
o
r
e
an
d
m
o
r
e
p
o
p
u
lar
ity
d
u
e
to
th
e
p
o
ten
tial
o
f
t
h
e
co
m
b
in
atio
n
o
f
p
r
ec
is
e
lo
ca
l
f
ea
tu
r
e
ex
tr
ac
tio
n
an
d
t
h
e
g
lo
b
al
co
n
te
x
t.
T
h
e
p
o
p
u
l
ar
ity
o
f
C
NN
-
tr
an
s
f
o
r
m
er
h
y
b
r
id
s
in
th
e
b
est
-
p
er
f
o
r
m
in
g
m
o
d
els in
th
e
r
ec
e
n
t y
ea
r
s
s
p
ea
k
s
f
o
r
its
elf
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
:
1
3
8
2
-
1
3
9
8
1390
c.
T
h
er
e
is
a
g
r
o
win
g
tr
e
n
d
in
th
e
u
s
e
o
f
Mu
lti
-
m
o
d
al
an
d
T
ask
-
Awa
r
e
ar
ch
itectu
r
es,
esp
ec
i
ally
in
th
e
f
u
s
io
n
o
f
d
if
f
er
en
t
d
ata
s
o
u
r
ce
s
(
ME
FNet
f
o
r
PET
/C
T
)
an
d
th
e
co
m
b
in
atio
n
o
f
th
e
s
eg
m
e
n
tatio
n
task
with
o
th
er
clin
ical
o
b
jectiv
es,
s
u
ch
as su
r
v
iv
al
an
aly
s
is
(
R
E
C
I
STSu
r
v
)
.
6
.
3
.
M
o
da
lity
-
s
pecif
ic
o
bs
er
v
a
t
io
ns
a
nd
clinica
l r
elev
a
nce
Mo
d
ality
-
s
p
ec
if
ic
o
b
s
er
v
atio
n
s
an
d
clin
ical
r
elev
an
ce
u
s
ed
i
n
clu
d
e
:
a.
CT
-
ce
n
tr
ic
d
ev
elo
p
m
en
t
:
C
o
m
p
u
ted
to
m
o
g
r
a
p
h
y
r
em
ain
s
th
e
d
o
m
i
n
an
t
im
ag
in
g
m
o
d
ality
,
with
th
e
m
ajo
r
ity
o
f
r
e
v
iewe
d
m
o
d
els
d
ev
elo
p
ed
a
n
d
e
v
alu
ated
o
n
C
T
d
atasets
.
T
h
is
r
ef
lects
C
T
’
s
wid
esp
r
ea
d
clin
ical
av
ailab
ilit
y
an
d
ce
n
tr
a
l r
o
le
in
liv
er
ca
n
ce
r
m
a
n
ag
em
en
t.
b.
MRI
an
d
alter
n
ati
v
e
m
o
d
al
ities
:
MRI
-
b
ased
ap
p
r
o
ac
h
e
s
s
u
ch
as
UGCMANe
t
[
2
4
]
d
e
m
o
n
s
tr
ate
co
m
p
etitiv
e
r
ep
o
r
ted
p
er
f
o
r
m
an
ce
b
y
ex
p
l
o
itin
g
m
u
lti
-
co
n
tr
ast
o
r
d
y
n
am
ic
s
eq
u
en
ce
s
.
Mo
d
els
tar
g
etin
g
u
ltra
s
o
u
n
d
[
2
8
]
an
d
PET
/C
T
[
3
0
]
ad
d
r
ess
s
p
ec
if
ic
clin
ica
l
n
ich
es,
alth
o
u
g
h
th
ey
o
f
te
n
f
ac
e
m
o
d
ality
-
s
p
ec
if
ic
ch
allen
g
es a
f
f
ec
tin
g
s
eg
m
en
tatio
n
c
o
n
s
is
ten
cy
.
c.
W
o
r
k
f
lo
w
-
o
r
ien
te
d
m
o
d
elin
g
:
Sev
er
al
s
tu
d
ies
ex
p
licitly
al
ig
n
m
o
d
el
d
esig
n
with
clin
ical
wo
r
k
f
lo
ws,
in
clu
d
in
g
C
o
n
v
NeX
t
-
2
U
[
7
]
f
o
r
Y
-
9
0
r
a
d
io
em
b
o
lizatio
n
p
la
n
n
in
g
an
d
L
A
-
R
esUNet
[
2
0
]
f
o
r
lo
n
g
itu
d
in
al
tu
m
o
r
m
o
n
ito
r
in
g
.
T
h
ese
ef
f
o
r
ts
s
u
g
g
est
in
c
r
ea
s
in
g
awa
r
en
ess
o
f
tr
a
n
s
latio
n
al
co
n
s
id
er
atio
n
s
b
ey
o
n
d
alg
o
r
ith
m
ic
ac
cu
r
ac
y
.
6
.
4
.
P
er
s
is
t
ing
cha
lleng
es a
cr
o
s
s
s
t
ud
ie
s
Desp
ite
n
o
tab
le
p
r
o
g
r
ess
,
s
ev
er
al
ch
allen
g
es c
o
n
s
is
ten
tly
em
er
g
e
ac
r
o
s
s
th
e
liter
atu
r
e:
a.
Data
av
ailab
ilit
y
:
Hig
h
-
ca
p
ac
i
ty
m
o
d
els,
p
ar
ticu
lar
ly
t
r
an
s
f
o
r
m
er
-
b
ased
a
n
d
h
y
b
r
id
ar
c
h
itectu
r
es,
r
ely
o
n
lar
g
e,
an
n
o
tated
d
atasets
,
wh
ich
r
em
ain
lim
ited
i
n
m
an
y
clin
i
ca
l settin
g
s
.
b.
C
o
m
p
u
tatio
n
al
ac
ce
s
s
ib
ilit
y
:
I
n
cr
ea
s
ed
m
o
d
el
co
m
p
lex
ity
c
an
h
in
d
er
wid
esp
r
ea
d
ad
o
p
tio
n
,
esp
ec
ially
in
in
s
titu
tio
n
s
with
lim
ited
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
.
c.
Gen
er
aliza
tio
n
an
d
r
o
b
u
s
tn
ess
:
R
ep
o
r
ted
p
er
f
o
r
m
a
n
ce
o
f
te
n
v
ar
ies
ac
r
o
s
s
d
atasets
,
h
ig
h
lig
h
tin
g
s
en
s
itiv
ity
to
s
ca
n
n
er
d
if
f
er
en
ce
s
,
im
a
g
in
g
p
r
o
t
o
co
ls
,
an
d
p
o
p
u
latio
n
d
iv
er
s
ity
.
d.
Sm
all
lesi
o
n
s
eg
m
en
tatio
n
:
Acc
u
r
ately
d
elin
ea
tin
g
s
m
all
o
r
lo
w
-
co
n
tr
ast
tu
m
o
r
s
co
n
tin
u
es
to
b
e
ch
allen
g
in
g
f
o
r
m
an
y
e
x
is
tin
g
ar
ch
itectu
r
es.
6
.
5
.
Su
mm
a
ry
a
nd
t
ra
ns
it
io
n
Ov
er
all,
th
e
liter
atu
r
e
s
u
g
g
ests
a
p
r
o
g
r
ess
io
n
to
war
d
s
b
a
lan
cin
g
th
e
f
o
cu
s
awa
y
f
r
o
m
th
e
s
o
le
n
o
v
elty
o
f
ar
c
h
itectu
r
e
an
d
t
o
war
d
s
a
m
o
r
e
well
-
r
o
u
n
d
ed
ev
alu
atio
n
o
f
ac
c
u
r
ac
y
,
ef
f
i
cien
cy
,
an
d
d
ep
l
o
y
ab
ilit
y
.
Alth
o
u
g
h
th
e
h
y
b
r
id
a
n
d
en
s
em
b
le
m
o
d
els ar
e
ce
r
tain
ly
an
in
ter
esti
n
g
an
d
ev
o
lv
i
n
g
s
p
ac
e,
p
er
h
a
p
s
th
e
m
o
s
t
p
r
o
m
is
in
g
clin
ical
im
p
lem
en
tatio
n
m
ay
b
e
with
th
e
ef
f
icien
t
C
NN
ar
ch
itectu
r
es
an
d
atten
tio
n
m
ec
h
an
is
m
s
.
T
h
e
r
ec
u
r
r
in
g
c
h
allen
g
es
p
r
esen
ted
h
e
r
e
ar
e
th
e
p
r
ec
u
r
s
o
r
to
th
e
o
p
en
p
r
o
b
lem
s
an
d
f
u
tu
r
e
r
esear
ch
d
ir
ec
tio
n
s
p
r
esen
ted
i
n
th
e
f
o
llo
win
g
s
ec
tio
n
.
7.
F
O
CUSE
D
R
E
SU
L
T
S AN
D
P
RACTI
CAL R
E
CO
M
M
E
NDAT
I
O
NS
B
y
lo
o
k
in
g
at
th
e
r
esu
lts
r
ep
o
r
ted
in
3
8
d
if
f
er
en
t
m
o
d
els
f
o
r
liv
er
an
d
liv
er
tu
m
o
r
s
eg
m
en
t
atio
n
th
at
ar
e
co
n
s
id
er
ed
d
ee
p
lear
n
in
g
m
o
d
els
(
p
u
b
licatio
n
s
f
r
o
m
2
0
2
3
-
2
0
2
5
)
,
s
o
m
e
in
ter
es
tin
g
an
d
ap
p
licab
le
o
b
s
er
v
atio
n
s
r
eg
a
r
d
in
g
th
e
b
a
lan
ce
b
etwe
en
ac
cu
r
ac
y
an
d
c
o
m
p
u
tatio
n
al
r
eq
u
ir
e
m
en
ts
ar
e
m
ad
e.
I
n
s
tead
o
f
p
r
o
v
id
i
n
g
r
a
n
k
in
g
s
an
d
s
co
r
es,
s
o
m
e
ex
am
p
les
ar
e
p
r
o
v
id
e
d
f
r
o
m
th
e
liter
atu
r
e
th
at
h
ig
h
lig
h
t
th
e
b
alan
ce
th
at
ex
is
ts
in
r
ea
l
-
wo
r
ld
s
ce
n
ar
io
s
.
7
.
1
.
O
bs
er
v
ed
a
cc
ura
c
y
–
co
m
plex
it
y
t
r
a
de
-
o
f
f
s
B
u
t
r
ec
en
t
wo
r
k
h
as
also
s
ee
n
th
e
r
is
e
o
f
h
y
b
r
id
C
NN
-
tr
an
s
f
o
r
m
er
m
o
d
els
an
d
atten
tio
n
-
b
ased
ar
ch
itectu
r
es,
wh
ich
ar
e
ac
h
i
ev
in
g
ev
e
n
h
ig
h
er
s
eg
m
e
n
tatio
n
ac
cu
r
ac
y
,
p
a
r
ticu
lar
ly
in
co
m
p
lex
ca
s
es
lik
e
tu
m
o
r
s
.
T
o
n
a
m
e
a
f
ew,
R
esTr
an
s
UNe
t
is
a
C
NN
-
tr
an
s
f
o
r
m
er
m
o
d
el
th
at
h
as
r
ep
o
r
ted
h
ig
h
er
Dice
s
co
r
es
i
n
liv
er
an
d
tu
m
o
r
s
eg
m
e
n
tatio
n
.
Ho
wev
er
,
th
e
ac
c
u
r
ac
y
in
c
r
ea
s
e
also
h
as
a
p
r
ice
to
p
ay
in
te
r
m
s
o
f
h
ig
h
er
GPU
m
em
o
r
y
u
s
ag
e
an
d
tr
ain
in
g
c
o
m
p
lex
ity
.
On
t
h
e
o
t
h
er
h
an
d
,
atten
tio
n
-
b
ased
C
NNs
lik
e
MA
PF
UNe
t
ac
h
iev
e
g
o
o
d
ac
cu
r
ac
y
in
s
eg
m
e
n
tin
g
ev
en
s
m
aller
o
r
less
co
n
tr
asted
lesi
o
n
s
th
r
o
u
g
h
m
u
lti
-
atten
tio
n
m
ec
h
a
n
is
m
s
,
all
wh
ile
m
ain
tain
in
g
a
r
elativ
ely
m
o
d
er
ate
co
m
p
u
tatio
n
al
co
s
t
in
ter
m
s
o
f
GPU
m
em
o
r
y
u
s
ag
e.
B
u
t
C
NN
s
lik
e
UNe
t++
,
wh
ich
r
e
p
o
r
t
s
lig
h
tly
lo
wer
Dice
s
co
r
es
in
s
eg
m
en
tatio
n
,
s
till
r
em
ain
th
e
b
est
o
p
tio
n
in
ter
m
s
o
f
m
o
d
el
co
m
p
lex
ity
an
d
ea
s
e
o
f
d
ep
lo
y
m
e
n
t.
7
.
2
.
Co
nte
x
t
-
depend
ent
depl
o
y
m
ent
c
o
ns
idera
t
io
ns
T
h
e
r
ev
iewe
d
liter
atu
r
e
s
u
g
g
e
s
ts
th
at
th
e
s
u
itab
ilit
y
o
f
a
s
eg
m
en
tatio
n
m
o
d
el
is
h
i
g
h
ly
d
e
p
en
d
en
t
o
n
th
e
in
ten
d
ed
clin
ical
ap
p
licati
o
n
,
av
ailab
le
i
n
f
r
astru
ctu
r
e,
an
d
o
p
er
atio
n
al
co
n
s
tr
ain
ts
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Hyb
r
id
co
n
vo
lu
tio
n
a
l n
e
u
r
a
l
n
etw
o
r
k
–
tr
a
n
s
fo
r
mer m
o
d
els f
o
r
…
(
I
b
r
a
h
im
Mo
h
a
med
A
ttiy
a
)
1391
a.
Hig
h
-
r
eso
u
r
ce
e
n
v
ir
o
n
m
en
ts
:
Sev
er
al
s
tu
d
ies
ex
p
lo
r
e
h
y
b
r
id
o
r
atten
tio
n
-
b
ased
ar
c
h
itectu
r
es
(
e.
g
.
,
R
esTr
an
s
UNe
t,
MA
P
FUNet
[
4
]
)
i
n
r
esear
ch
o
r
ter
tiar
y
-
c
ar
e
s
ettin
g
s
wh
er
e
ad
v
an
ce
d
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
ar
e
av
ailab
le
a
n
d
s
e
g
m
en
tatio
n
p
r
ec
is
io
n
is
a
p
r
im
ar
y
co
n
ce
r
n
.
b.
R
eso
u
r
ce
-
co
n
s
tr
ain
ed
o
r
h
i
g
h
-
th
r
o
u
g
h
p
u
t
s
ettin
g
s
:
L
ig
h
tweig
h
t
C
NN
-
b
ased
m
o
d
els
s
u
ch
as
UNe
t++
[
2
]
o
r
L
iv
e
r
Net
[
1
5
]
ar
e
f
r
eq
u
en
tl
y
d
is
cu
s
s
ed
as
p
r
ac
tical
alter
n
ativ
es
d
u
e
t
o
th
eir
f
aster
i
n
f
er
en
ce
tim
es
an
d
lo
wer
h
ar
d
war
e
r
eq
u
i
r
em
en
ts
.
c.
Sp
ec
ialized
clin
ical
s
ce
n
ar
io
s
:
C
er
tain
m
o
d
els
ar
e
d
esig
n
e
d
f
o
r
s
p
ec
if
ic
wo
r
k
f
lo
ws,
in
clu
d
i
n
g
lo
n
g
itu
d
in
al
tu
m
o
r
m
o
n
ito
r
in
g
(
L
A
-
R
esUNet
[
2
0
]
)
,
m
u
ltimo
d
al
im
ag
in
g
in
teg
r
atio
n
(
R
esUNet
–
I
n
ce
p
tio
n
V4
[
8
]
,
ME
FNet
[
3
0
]
)
,
a
n
d
in
ter
v
en
tio
n
al
p
lan
n
in
g
ap
p
licatio
n
s
(
C
o
n
v
NeX
t
-
2
U
[
7
]
).
T
h
ese
ex
am
p
les
illu
s
tr
ate
h
o
w
ar
ch
itectu
r
al
ch
o
ices
alig
n
with
d
if
f
er
en
t
clin
ical
an
d
o
p
er
at
io
n
al
n
ee
d
s
,
r
ath
er
th
an
in
d
icatin
g
th
e
u
n
iv
er
s
al
s
u
p
er
io
r
ity
o
f
a
n
y
s
in
g
le
m
o
d
el
.
7
.
3
.
H
u
m
a
n
-
in
-
t
he
-
lo
o
p inte
g
ra
t
io
n
Fro
m
th
e
ab
o
v
e
r
esear
ch
,
it
is
clea
r
th
at
th
er
e
is
a
tr
en
d
o
f
i
n
cr
ea
s
in
g
in
ter
est
in
th
e
in
teg
r
a
tio
n
o
f
th
e
au
to
m
ated
s
eg
m
en
tatio
n
m
o
d
els
in
th
e
h
u
m
an
-
in
-
th
e
-
lo
o
p
(
HI
T
L
)
clin
ical
en
v
ir
o
n
m
en
t.
I
n
th
is
s
y
s
tem
,
th
e
AI
-
b
ased
s
eg
m
en
tatio
n
s
ar
e
p
r
o
v
id
e
d
as
a
f
ir
s
t
g
u
ess
,
wh
ich
is
th
en
f
u
r
th
er
r
e
v
iewe
d
b
y
th
e
ex
p
e
r
t
r
ad
io
lo
g
is
ts
.
T
h
is
ty
p
e
o
f
co
ll
ab
o
r
ativ
e
s
y
s
tem
is
b
e
n
ef
icial
in
ter
m
s
o
f
clin
ical
s
af
ety
,
a
cc
o
u
n
tab
ilit
y
,
a
n
d
s
y
s
tem
im
p
r
o
v
em
en
t
th
r
o
u
g
h
ex
p
er
t
f
ee
d
b
ac
k
,
esp
ec
ially
in
co
m
p
lex
an
d
am
b
ig
u
o
u
s
s
ce
n
ar
io
s
.
T
h
e
HI
T
L
in
teg
r
atio
n
o
f
th
e
AI
-
b
ased
s
y
s
tem
is
co
n
s
id
er
ed
a
cr
u
cial
p
ar
t
o
f
th
e
s
y
s
tem
in
o
r
d
e
r
to
p
r
o
v
id
e
a
s
af
e
an
d
ef
f
ec
tiv
e
s
o
lu
tio
n
in
th
e
clin
ic
al
en
v
ir
o
n
m
en
t.
8.
CH
AL
L
E
NG
E
S AN
D
O
P
E
N
RE
S
E
ARCH
P
RO
B
L
E
M
S
Alth
o
u
g
h
th
er
e
h
a
v
e
b
ee
n
s
ig
n
if
ican
t
a
d
v
an
ce
m
en
ts
in
t
h
e
ap
p
licatio
n
o
f
d
ee
p
lear
n
in
g
-
b
ased
s
eg
m
en
tatio
n
o
f
th
e
liv
e
r
a
n
d
liv
er
tu
m
o
r
s
,
th
e
r
e
a
r
e
s
till
a
n
u
m
b
er
o
f
ch
allen
g
es
an
d
r
esear
ch
q
u
esti
o
n
s
r
em
ain
in
g
,
w
h
ich
lim
it th
eir
a
p
p
licatio
n
in
a
clin
ical
e
n
v
ir
o
n
m
en
t.
8
.
1
.
Da
t
a
a
v
a
ila
bil
it
y
a
nd
a
n
no
t
a
t
io
n
T
h
e
ab
ilit
y
to
b
u
ild
r
o
b
u
s
t
d
e
ep
lear
n
i
n
g
m
o
d
els
d
ep
en
d
s
i
n
a
c
r
u
cial
m
an
n
er
o
n
th
e
av
a
ilab
ilit
y
o
f
lar
g
e
q
u
a
n
titi
es
o
f
well
-
an
n
o
t
ated
m
ed
ical
im
a
g
in
g
d
ata.
H
o
wev
er
,
th
e
cr
ea
tio
n
o
f
d
iv
er
s
e
m
u
lticen
ter
d
ata
s
ets
with
co
n
s
is
ten
t
an
n
o
tatio
n
is
h
in
d
er
ed
b
y
p
atien
t
p
r
iv
a
cy
co
n
ce
r
n
s
,
im
a
g
in
g
p
r
o
to
co
l
v
ar
iab
ilit
y
,
an
d
th
e
lab
o
r
-
in
ten
s
iv
e
n
atu
r
e
o
f
m
an
u
al
an
n
o
tatio
n
.
As a
r
esu
lt,
th
e
d
iv
er
s
ity
o
f
t
h
e
d
ata
s
ets m
ay
b
e
lim
ited
.
8
.
2
.
Co
m
pu
t
a
t
io
na
l
c
o
m
plex
it
y
a
nd
re
s
o
urce
co
ns
t
ra
ints
T
h
er
e
a
r
e
m
a
n
y
ad
v
a
n
ce
d
m
o
d
els,
esp
ec
ially
t
h
o
s
e
wi
th
th
e
u
s
e
o
f
atten
tio
n
m
e
ch
an
is
m
s
,
tr
an
s
f
o
r
m
er
s
,
an
d
en
s
em
b
le
ap
p
r
o
ac
h
es,
wh
ich
o
f
ten
ca
ll
f
o
r
co
n
s
id
er
ab
le
c
o
m
p
u
tatio
n
al
r
e
q
u
ir
em
en
ts
d
u
r
in
g
th
e
tr
ain
in
g
an
d
ev
alu
atio
n
p
r
o
ce
s
s
.
T
h
u
s
,
th
er
e
is
alwa
y
s
th
e
n
ee
d
t
o
d
e
v
elo
p
m
o
d
els
th
at
en
s
u
r
e
th
e
tr
ad
eo
f
f
b
etwe
en
s
eg
m
en
tatio
n
ac
c
u
r
ac
y
an
d
c
o
m
p
u
tatio
n
al
ef
f
icien
c
y
an
d
tr
ac
ta
b
ilit
y
.
8
.
3
.
G
ener
a
liza
t
io
n a
nd
do
m
a
in a
da
pta
t
i
o
n
Gen
er
aliza
tio
n
ac
r
o
s
s
d
if
f
e
r
en
t
im
ag
in
g
p
r
o
to
c
o
ls
,
s
ca
n
n
er
v
en
d
o
r
s
,
an
d
p
atien
t
d
em
o
g
r
a
p
h
ics
.
T
h
e
is
s
u
e
o
f
g
en
er
aliza
tio
n
ac
r
o
s
s
d
iv
er
s
e
im
ag
in
g
p
r
o
to
c
o
ls
,
s
ca
n
n
er
m
an
u
f
ac
tu
r
e
r
s
,
an
d
p
atien
t
p
o
p
u
latio
n
s
s
till
r
em
ain
s
a
m
ajo
r
co
n
ce
r
n
.
Alth
o
u
g
h
v
a
r
io
u
s
tech
n
iq
u
es,
s
u
ch
as d
o
m
ain
ad
ap
tatio
n
,
tr
an
s
f
e
r
lear
n
in
g
,
an
d
d
ata
au
g
m
en
tatio
n
,
h
av
e
s
h
o
wn
p
r
o
m
is
e,
th
ey
ar
e
n
o
t e
q
u
ally
ef
f
e
ctiv
e
ac
r
o
s
s
d
if
f
er
en
t scen
a
r
io
s
.
8
.
4
.
I
nte
rpre
t
a
bil
it
y
a
nd
ex
p
la
ina
bil
it
y
T
h
e
in
ter
p
r
eta
b
ilit
y
o
f
d
ee
p
le
ar
n
in
g
m
o
d
els
is
a
m
ajo
r
r
o
a
d
b
lo
ck
to
th
e
ad
o
p
tio
n
o
f
d
ee
p
lear
n
in
g
m
o
d
els
in
t
h
e
h
ea
lth
ca
r
e
s
ec
to
r
,
co
n
s
id
er
in
g
th
at
h
ea
lth
ca
r
e
p
r
o
f
ess
io
n
als
r
eq
u
ir
e
tr
a
n
s
p
ar
en
cy
a
n
d
t
r
u
s
t
with
r
eg
ar
d
to
a
u
to
m
ated
d
ec
is
io
n
-
s
u
p
p
o
r
t
s
y
s
tem
s
.
Alth
o
u
g
h
r
ec
en
t
r
esear
ch
in
to
u
n
ce
r
tain
ty
esti
m
atio
n
an
d
ex
p
lain
ab
le
ar
tific
ial
in
tellig
en
ce
(
XAI
)
h
as
p
r
o
p
o
s
ed
p
r
o
m
is
in
g
av
en
u
es
to
ex
p
lo
r
e,
m
o
r
e
r
esear
ch
is
n
ec
ess
ar
y
to
en
s
u
r
e
th
at
t
h
e
in
s
ig
h
ts
ar
e
in
ter
p
r
etab
le
with
o
u
t c
o
m
p
r
o
m
is
in
g
s
eg
m
en
tatio
n
ac
cu
r
ac
y
.
8
.
5
.
M
ulti
-
m
o
da
lit
y
a
nd
clinica
l w
o
rk
f
lo
w
inte
g
r
a
t
io
n
T
h
e
in
teg
r
atio
n
o
f
m
u
ltimo
d
al
in
f
o
r
m
atio
n
,
s
u
ch
as
p
o
s
itro
n
em
is
s
io
n
to
m
o
g
r
ap
h
y
,
m
ag
n
etic
r
eso
n
an
ce
im
a
g
in
g
,
an
d
g
en
o
m
ic
in
f
o
r
m
atio
n
,
in
t
o
e
x
is
tin
g
co
m
p
u
ted
to
m
o
g
r
a
p
h
y
-
b
ased
s
eg
m
en
tatio
n
s
ch
em
es
h
as
s
h
o
wn
p
r
o
m
is
e
f
o
r
i
n
cr
ea
s
in
g
clin
ical
r
elev
an
ce
an
d
p
r
ec
is
io
n
.
Ho
wev
er
,
t
h
er
e
ar
e
also
s
o
m
e
ch
allen
g
es
ass
o
ciate
d
with
m
u
ltimo
d
al
in
f
o
r
m
atio
n
f
u
s
io
n
,
i
n
clu
d
in
g
d
ata
h
ar
m
o
n
izatio
n
,
s
y
s
tem
co
m
p
lex
ity
,
an
d
r
eliab
ilit
y
.
Seam
less
in
teg
r
atio
n
in
to
e
x
is
tin
g
clin
ical
wo
r
k
f
lo
w,
in
clu
d
in
g
p
ictu
r
e
ar
ch
iv
i
n
g
an
d
co
m
m
u
n
icatio
n
s
y
s
tem
c
o
m
p
a
tib
ilit
y
,
is
also
a
n
o
n
-
tr
iv
ial
p
r
o
b
lem
.
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