###### Abstract

The CALICE Semi-Digital Hadronic CALorimeter (SDHCAL) is the first
technological prototype in a family of high-granularity calorimeters developed
by the CALICE Collaboration to equip the experiments of future lepton
colliders. The SDHCAL is a sampling calorimeter using stainless steel for
absorber and Glass Resistive Plate Chambers (GRPC) as a sensitive medium. The
GRPC are read out by 1 cm $\times$ 1 cm pickup pads combined to a multi-
threshold electronics. The prototype was exposed to hadron beams in both the
CERN PS and the SPS beamlines in 2015 allowing the test of the SDHCAL in a
large energy range from 3 GeV to 80 GeV. After introducing the method used to
select the hadrons of our data and reject the muon and electron contamination,
we present the energy reconstruction approach that we apply to the data
collected from both beamlines and we discuss the response linearity and the
energy resolution of the SDHCAL. The results obtained in the two beamlines
confirm the excellent SDHCAL performance observed with the data collected with
the same prototype in the SPS beamline in 2012. They also show the stability
of the SDHCAL in different beam conditions and different time periods.

Energy reconstruction of hadronic showers at the CERN PS and SPS using the
Semi-Digital Hadronic Calorimeter The CALICE Collaboration D. Boumediene
Université Clermont Auvergne, Université Blaise Pascal, CNRS/IN2P3, LPC, 4 Av.
Blaise Pascal, TSA/CS 60026, F-63178 Aubière, France  A. Pingault, M. Tytgat
Ghent University, Department of Physics and Astronomy, Proeftuinstraat 86,
B-9000 Gent, Belgium  Y. W. Baek, D-W. Kim111Now at Seoul National University
Hospital, S. C. Lee, B. G. Min, S. W Park
Gangneung-Wonju National University Gangneung 25457, South Korea  Y. Deguchi,
K. Kawagoe, Y. Miura, R. Mori, I. Sekiya, T. Suehara, T. Yoshioka
Department of Physics and Research Center for Advanced Particle Physics,
Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan  L.
Caponetto, C. Combaret, G. Garillot, G. Grenier, J-C. Ianigro, T. Kurca, I.
Laktineha , B. Liua , B. Li, N. Lumb, H. Mathez, L. Mirabito, A. Steen222Now
at NTU
Univ Lyon, Univ CLaude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon, F-69622
Villeurbanne, France  E. Calvo Alamillo, C. Carrillo, M.C. Fouz, H. Garcia
Cabrera, J. Marin, J. Navarrete, J. Puerta Pelayo, A. Verdugo
CIEMAT, Centro de Investigaciones Energeticas, Medioambientales y
Tecnologicas, Madrid, Spain  F. Corriveau
Department of Physics, McGill University, Ernest Rutherford Physics Bldg.,
3600 University Ave., Montréal, Québec Canada H3A 2T8  L. Emberger, C. Graf,
L.M.S de Silva, F. Simon, C. Winter
Max-Planck-Institut für Physik, Föhringer Ring 6, D-80805 Munich, Germany  J.
Bonis, D. Breton, P. Cornebise, A. Gallas, J. Jeglot, A. Irles, J. Maalmi, R.
Pöschl, A. Thiebault, F. Richard, D. Zerwas
Universit Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France  J. Cvach, M.
Janata, M. Kovalcuk, J. Kvasnicka, I. Polak, J. Smolik, V. Vrba, J. Zalesak,
J. Zuklin
Institute of Physics, The Czech Academy of Sciences, Na Slovance 2, CZ-18221
Prague 8, Czech Republic  Y.Y. Duan, S. Li, J. Guo, J.F. Hu, F. Lagarde, B.
Liu, Q.P. Shen, X. Wang, W.H. Wu, H.J. Yang, Y.F. Zhu
Tsung-Dao Lee Institute, Institute of Nuclear and Particle Physics, School of
Physics and Astronomy, Shanghai Jiao Tong University, Key Laboratory for
Particle Physics, Astrophysics and Cosmology (Ministry of Education), Shanghai
Key Laboratory for Particle Physics and Cosmology, 800 Dongchuan Road,
Shanghai, 200240, P. R. China  L. Emberger, C. Graf, L.M.S de Silva, F. Simon,
C. Winter
Max-Planck-Institut für Physik, Föhringer Ring 6, D-80805 Munich, Germany  a
Corresponding authors
E-mail:  Calice Collaboration

##  1 Introduction

The Semi-Digital Hadronic CALorimeter (SDHCAL) [1] is the first of a series of
technological high-granularity prototypes developed by the CALICE
collaboration. The SDHCAL is a sampling calorimeter using stainless steel for
absorber and Glass Resistive Plate Chambers (GRPC) for its sensitive medium.
The SDHCAL is designed to be as compact as possible with its mechanical
structure being part of the absorber. The GRPC and the readout electronics are
conceived to achieve minimal dead zonesc̃iteSDHCAL. This design renders the
SDHCAL optimal for the application of the Particle Flow Algorithm (PFA)
techniques [2, 3, 4].

The SDHCAL ( 1) comprises 48 active layers, each of them equipped with a 1 m
$\times$ 1 m GRPC and an Active Sensor Unit (ASU) of the same size hosting on
one side (the one in contact with the GRPC) pickup pads of 1 cm $\times$ 1 cm
size each and 144 HARDROC2 ASICs [5] on the other side (Fig. 2). The GRPC and
the ASU are assembled within a cassette made of two stainless steel plates,
2.5 mm thick each. The 48 cassettes are inserted in a self-supporting
mechanical structure made of 49 plates, 15 mm thick each, of the same material
as the cassettes, bringing the total absorber thickness to 20 mm per layer.
The empty space between two consecutive plates is 13 mm to allow the insertion
of one cassette of 11 mm thickness. In total, the SDHCAL represents about 6
interaction lengths $\lambda_{I}$. The HARDROC2 ASIC has 64 channels to read
out 64 pickup pads. Each channel has three parallel digital circuits whose
parameters can be configured to provide 2-bit encoded information per channel
indicating if the charge seen by each pad has passed any of the three
different thresholds associated to each digital circuit. This multi-threshold
readout is used to improve on the energy reconstruction of hadronic showers at
high energy ($>30$ GeV) with respect to the simple binary readout mode as
explained in Ref. [6].

The SDHCAL was exposed to different kinds of particles at the CERN SPS
beamline in 2012 and its performance was studied in the the energy range above
10 GeV [6] since lower particle energy values are difficult to obtain in the
SPS beamline standard configurations. To study its performance in different
beam conditions and at lower energies, the SDHCAL was exposed to hadrons both
in the SPS and PS beamlines in 2015. It was first exposed to negatively
charged pion beams of 3, 4, 5, 6, 7, 8, 9, 10 and 11 GeV at the PS beamline
and then to positively charged hadrons of 10, 20, 30, 40, 50, 60, 70 and 80
GeV at the SPS beamline. In both cases, about 10000 events were collected for
each energy point.

In this paper, section 2 gives the details of the collected beam data as well
as the samples of simulated events used for comparison. The pion selection and
the muon and electron contamination rejection using the MultiVariate Analysis
(MVA) technique known as Boosted Decision Trees (BDT) to separate pion and
election showers is given in section 3. Energy reconstruction of the selected
pion events is discussed in section 4. Finally, in section 5 we present the
uncertainties related to the energy reconstruction of the collected data.

Figure 1: A picture of the SDHCAL prototype in beam test.

Figure 2: Cross-section of one of the prototype’s 48 active layers.

##  2 Simulation

The simulation model of SDHCAL, based on Geant4.9.6 toolkit package [7], was
developed including the interactions of different kinds of particles such as
muons, electrons and pions in the SDHCAL prototype. The simulation takes into
account the operation conditions of the GRPC to which an effective high
voltage of 7.2 kV was applied. It uses the same values of 0.114, 5 and 15 pC
that are used by the SDHCAL readout system for the first, the second and the
third threshold respectively.

Among the different Geant4 physics lists that were used to compare the
simulation with the beam data collected in 2012, FTF_BIC was found to provide
the best agreement [8]. Therefore, we use this physics list in this work to
simulate events with different kinds of particles having the same energies and
impinging on the prototype in the same area as those to which the SDHCAL was
exposed during the beam test campaigns at the SPS and PS beamlines. The
simulated events are then used to optimize the selection of pions in data by
rejecting muon and electron beam contamination. They are also used to estimate
possible biases that may influence the data energy reconstruction of hadronic
showers in terms of linearity and resolution.

##  3 Pion events selection

The pion samples of both SPS and PS beams are contaminated by two kinds of
particles: electrons and muons. The muon contamination exists in all samples
from 3 to 80 GeV. This includes two different types of muons: cosmic muons and
beam muons. The latter are generated by pions decaying before arriving at the
prototype. Concerning the electron contamination, it is negligible in pion
samples from 6 to 11 GeV in the PS pion beam but still present in the energy
range below 6 GeV at the level of a few percent of the beam content. The
electron contamination is also present in the SPS pion beam, especially in the
energy range between 10 and 50 GeV [6]. The muon rejection is rather easy due
to their track-like shape that distinguishes them clearly from the hadronic
showers in the SDHCAL. The electron rejection is harder. Electron showers, in
particular at low energy, are similar to the pion ones. Although in both PS
and SPS, an electron stopper made of a few millimeters thick lead plate was
used, this does not allow to completely eliminate the electron contamination.
Inspired by Refs. [9, 10], we propose to use the BDT technique to reject the
electron background of our pion samples in an improved way with respect to the
one used in a previous analysis applied to data collected by SDHCAL in 2015 at
the SPS beamline [11] in the energy range between 10 and 80 GeV.

###  3.1 Electron contamination rejection using Boosted Decision Tree (BDT)

As mentioned in the previous section, for the 6 to 11 GeV pion runs, the
electron contamination is negligible in the PS pion beam. However for the 3 to
5 GeV pion runs, the electron contamination is expected to be present due to
the PS beamline structure. Therefore, it is necessary to check the electron
contamination and to eliminate it. Thanks to the high granularity of the
SDHCAL prototype, we can use the BDT method to exploit the three dimensional
shape of both the electromagnetic and the hadronic showers to classify the
electron and pion events in our prototype. The BDT method is one of the most
powerful and widely used in high energy physics for classification tasks. The
TMVA package [12] contains a standardised implementation of this technique. We
adopt this package to build our BDT model to reject the electron
contamination.

####  3.1.1  BDT input variables

Based on the difference in topology between electromagnetic and hadronic
showers, we choose eight variables as inputs of the BDT model to help
discriminate pion against electron events. Hereafter, a description of each of
these variables is given:

  * •

First layer of the shower (Begin): To define the layer in which the shower
starts, we look for the first layer along the incoming particle direction
which contains at least 4 fired pads. To eliminate fake shower starts due to
accidental noise or a locally high multiplicity, the following 3 layers after
the first layer are also required to have more than 4 fired pads (hits) for
each of them. If no layer fulfils this, a value of -10 is assigned to the
variable. Since each layer of the SDHCAL represents about 1.2 radiation
lengths ($X_{0}$), electromagnetic showers start developing in the first
layers. For pions, their interaction probability density is given by
$1-\exp{(-\frac{l}{\lambda_{I}})}$ where $l$ is the length of the pion
trajectory in the calorimeter medium before interacting. Figure 3 shows the
distribution of the first layer of the shower in the SDHCAL prototype for
pions and electrons as obtained from the simulation.

Figure 3:  Distribution of the first layer number of pion and electron showers
in the PS energy range of 1-12 GeV (left) and the SPS energy range of 10-80
GeV (right) of pions and electrons as given by the simulation. The red line
corresponds to pions and the black one to electrons.

  * •

Number of track segments in the shower (nTrack): Applying the Hough Transform
(HT) method to single out the tracks in each event as described in Ref. [13],
we obtain the number of track segments in the pion, electron and muon events.
A HT-based segment candidate is considered as a track segment if there are
more than 6 aligned hits with not more than one layer separating two
consecutive hits. Electron showers feature almost no track segment while most
of the hadronic showers have at least one. The distribution of nTrack can be
seen in Fig. 4

Figure 4:  Distribution of the number of track segments in pion and electron
showers in the PS energy range of 1-12 GeV (left) and the SPS energy of 10-80
GeV (right) as given by the simulation. The red line corresponds to pions and
the black one to electrons.

  * •

Number of clusters of the shower (nCluster): All hits in a given layer are
clustered using a nearest-neighboring algorithm described in Ref. [1]. It
consists in merging in each GRPC plate the hits sharing a common edge. This
variable defines the number of clusters of the shower and its distribution is
shown in Fig. 5. Indeed, the compactness of the electromagnetic shower leads
to a reduced number of clusters in an electron shower with respect to that of
a pion shower of the same energy.

Figure 5:  Distribution of the number of clusters in pion and electron showers
in the PS energy range of 1-12 GeV (left) and the SPS range of 10-80 GeV
(right) as given by the simulation. The red line corresponds to pions and the
black one to electrons.

  * •

Ratio of the number of shower layers over the total number of fired layers
(nInteractingLayers/nLayers): This is the ratio between the number of the
shower layers defined as those in which the Root Mean Square (RMS) of the
hits’ position in the $x$-$y$ plane exceeds 5 cm in both $x$ and $y$
directions and the total number of layers with at least one hit. This variable
allows, as can be seen in Fig. 6, a good separation between pions and
electrons at low energy. It also allows an easy discrimination of muons
(including the radiative ones) against pions and electrons ones as will be
shown later.

Figure 6:  Distribution of the ratio of number of shower layers over the total
number of fired layers in pion and electron showers in the PS energy range of
1-12 GeV (left) and the SPS energy range of 10-80 GeV (right) as given by the
simulation. The red line corresponds to pions and the black one to electrons.

  * •

The average number of hits per fired layers (nHit/nLayer): This is the ratio
between the total number of fired pads over the number of layers with at least
one fired pad. The distribution of this variable is shown in Fig. 7.

Figure 7:  Distribution of the average number of hits per fired layers in pion
and electron showers in the PS energy range of 1-12 GeV (left) and the SPS
range of 10-80 GeV (right) as given by the simulation. The red line
corresponds to pions and the black one to electrons.

  * •

Shower density (Density): This is the average number of the neighbouring hits
located in the $3\times 3$ pads around one of the hits (including the hit
itself) in the given event. Figure 8 shows clearly that electromagnetic
showers are more compact than the hadronic ones as expected.

Figure 8:  Distribution of the density of pion and electron showers in the PS
energy range of 1-12 GeV (left) and the SPS range of 10-80 GeV (right) as
given by the simulation. The red line corresponds to pions and the black one
to electrons.

  * •

Shower radius (meanRadius): This is the RMS of hits distance with respect to
the event axis. To estimate the event axis, the average positions of the hits
in each of the ten first fired layers of an event are used to fit a straight
line. The straight line is then used as the event axis. The electromagnetic
shower being more compact than the hadronic shower, its radius is expected to
be smaller as can be seen in Fig. 9.

Figure 9:  Distribution of the meanRadius of pion and electron showers in the
PS energy range of 1-12 GeV (left) and the SPS range of 10-80 GeV (right) as
given by the simulation. The red line corresponds to pions and the black one
to electrons.

  * •

Ratio of the number of third-threshold hits over the total number of hits
(nHit3/nHit): The three thresholds indicate the amount of charge collected in
each pickup pad. The third one is set to single out the pads with high
collected charge that may be induced by the passage of many particles in the
cell associated to the pickup pad. The nHit3 is the number of third-threshold
hits in one event. The ratio of nHit3 to the total number of hits helps to
distinguish electromagnetic-like events and separate them from hadronic-like
ones since the relative number of hits with the third threshold is higher in
the former than in the latter due to the difference of their compactness. The
distribution of this ratio can be seen in Fig. 10.

Figure 10:  Distribution of the ratio of number of third threshold hits over
the total number of hits in pion and electron showers in the PS energy range
of 1-12 GeV (left) and the SPS range of 10-80 GeV (right) as given by the
simulation. The red line corresponds to pions and the black one to electrons.

####  3.1.2 Training and testing details of the BDT

For the training and testing process, 200000 pion and 200000 electron
simulated events are used to form a training set (66.7%) and a testing one
(33.3%). Another independent 400000 events including pions and electrons are
used as a validation set. The events are simulated evenly in the energy
interval between 1 and 80 GeV. The hyperparameters resulting from the BDT
optimisation procedure such as maxDepth333It controls the maximum depth of the
tree that will be created. It can also be described as the length of the
longest path from the tree root to a leaf. The root node is considered to have
a depth of 0. are described in Table 1.

Table 1: The chosen BDT hyperparameters. Option | Setting
---|---
Ntrees (Number of trees in the forest) | 1000
nCuts (Number of steps during node cut optimisation) | 20
MaxDepth (Max depth of the decision tree allowed) | 4

After feeding the eight topological variables to the BDT model using the
training and testing sets, the performance of our model is shown in Fig. 11.
It clearly shows the strong separation power between pions and electrons. At
the same time, the BDT response of the validation sets has very good agreement
with training ones. This confirms that our model is performing very well and
is not subject to overfitting. After applying the muon rejection cuts to be
explained in section to our in section 3.2 to our collected data, we apply our
BDT models on the selected events. Figure 12 shows the BDT output of 6 GeV
(left plot) and 11 GeV (right plot) pion runs which are supposed to be free of
electron contamination. The performance of the pion event selection matches
the one obtained with the simulated pion events quite well as shown in this
figure, thus confirming that our model is reliable. Figure 13 shows the result
of 3 GeV and 5 GeV pion beam runs which, in principle, may contain electron
contamination. From this figure, one can see that most of the events are
located in the region associated to pions and a good agreement is observed
between the data and the simulation even in the region of overlap between the
pions and the electrons. Therefore, the electron contamination in pion runs in
the 3-5 GeV energy range is rather small even if it is more important than
that of the PS higher energy runs. By requiring the BDT response to be larger
than 0.0, we select pure pion events.

Figure 11:  Distribution of the BDT output of training and validation set
using the simulated electron (black) and pion (red) events from 1 GeV to 80
GeV. The solid line is from training set while the dashed one is from
validation set.

Figure 12:  The BDT output of 6 GeV (left) and 11 GeV (right) beam runs after
muon rejection. The solid line is from pion beams and dashed one is from
training set.

Figure 13:  The BDT output of 3 GeV (left) and 5 GeV (right) beam runs after
muon rejection. The solid line is from pion beams and dashed one is from
training set.

###  3.2 Muon contamination rejection

The main contamination in our beam data is that of muons, including beam muons
and cosmic ones. To eliminate these two kinds of muons, we use the information
based on the different behaviours of muons and pions in the SDHCAL prototype.
Basically, muons cross the prototype and only leave a straight track in the
prototype like the one shown in Fig. 14. The mean of hits distance (described
by the variable meanRadius hereafter) of muon hits with respect to the global
event axis is thus very often less than 1.5 cm ($\approx$ 1.5 pads) as shown
in Fig. 15.

Figure 14:  Event display of one 6 GeV simulated muon with the green, blue and
red colour indicating the first, second and third threshold hits respectively.
The third threshold is often absent in muon tracks because of the small amount
of charge produced by muons in the RPC. Figure 15:  Distribution of the
meanRadius of shower by 1 GeV to 12 GeV muons as given by the simulation
(green). Electrons(black) and pions(red) meanRadius distributions are also
shown.

To eliminate most of the muon contamination, we require that the meanRadius is
greater than 2 cm. To further reduce the muon contamination, including the so-
called radiative muons that produce a few hit clusters around the muon track,
we require the ratio of the number of shower layers to the total number of
layers with at least one hit to be more than half.

To check the rejection power of the muon cuts, we apply it to dedicated muon
runs. Figure 16 shows the distribution of the number of hits before and after
muon rejection for 120 GeV muon runs. It clearly shows the rejection power of
this selection which is higher than 99.0%.

Figure 16:  Distribution of the number of hits for 20 GeV muon run before
(solid line) and after (dashed line) muon cut.

The result of the selection including the muon rejection and electron cut (BDT
response > 0.0) is shown in Fig. 17 and Fig. 18 for PS and SPS beam data runs
respectively.

Figure 17:  The number of hits for 3, 7 and 11 GeV pion beam runs before
(blue) and after (red) muon selection.

Figure 18:  The number of hits for 20, 40 and 60 GeV pion beam runs before
(blue) and after (red) muon selection.

##  4 Energy reconstruction

The rejection of electrons present in the pion data sample using the BDT but
also that of the muons allows us to have pure pion sample as explained in the
previous section. The selected pion events of the PS beam energy from 3 GeV to
11 GeV and those in the range of 10-80 GeV of the SPS, can then be used to
reconstruct energy.

Based on the information of the number of hits belonging to first threshold
(nHit1), second threshold (nHit2) and third threshold (nHit3), the hadronic
shower energy can be reconstructed as described in Ref. [6] using the
following formula:

$E_{reco}=\alpha\times nHit1+\beta\times nHit2+\gamma\times nHit3$ (1)

where $\alpha$, $\beta$ and $\gamma$ are weight factors which are parametrised
as second order polynomials of the total number of hits
$nHit=nHit1+nHit2+nHit3$:

$\begin{split}\alpha=\alpha_{1}+\alpha_{2}\times nHit+\alpha_{3}\times
nHit^{2}\\\ \beta=\beta_{1}+\beta_{2}\times nHit+\beta_{3}\times nHit^{2}\\\
\gamma=\gamma_{1}+\gamma_{2}\times nHit+\gamma_{3}\times nHit^{2}\end{split}$
(2)

The nine parameters $\alpha_{i=1,2,3}$, $\beta_{j=1,2,3}$ and
$\gamma_{k=1,2,3}$ are obtained, as described in Ref. [6], from a part of the
data samples of a few energy points by minimising the following $\chi^{2}$
expression:

$\chi^{2}=\sum_{i=1}^{N}\frac{(E_{beam}^{i}-E^{i}_{reco})^{2}}{\sigma^{2}_{i}}$
(3)

where the $E_{beam}^{i}$ denotes the beam energy and the $E^{i}_{reco}$ is the
reconstructed energy. $N$ is the number of total events and
$\sigma_{i}=\sqrt{E_{beam}^{i}}$ where the choice of
$\sigma=\sqrt{E_{beam}^{i}}$ is motivated by the fact that the expected energy
resolution is approximately given by the stochastic term:
$\frac{\sigma}{E_{beam}}=\frac{\alpha}{\sqrt{E_{beam}}}$.

Since the PS raw 10 GeV sample is almost free of electron contamination, it is
therefore expected to be less impacted by the BDT-based selection. On the
contrary, the SPS raw 10 GeV sample electron contamination is relatively
higher and could thus be impacted by the BDT-based selection. To check however
that this selection only eliminates the electrons without changing the pion
sample characteristics, the reconstructed energy of the PS 10 GeV sample
without BDT selection is compared to that of the SPS one after applying the
BDT selection. Fig. 19 shows the normalised reconstructed energy distribution
of these two samples. The good agreement between the two distributions
confirms the absence of bias of the BDT selection and its efficiency in
rejecting the electron contamination

Figure 19:  Reconstructed energy of the the PS 10 GeV pion sample and that of
the SPS 10 GeV sample after applying the BDT selection on the latter.

###  4.1 Energy resolution and linearity

The two purified samples; the one of 3-11 GeV and the one of 10-80 GeV
collected at the PS and the SPS beamline respectively, are then used to
reconstruct the pion energy in the SDHCAL following the method described in
Ref. [6].

The reconstructed energy distributions of 3, 7 and 11 GeV pion data samples
collected at PS are shown in Fig. 20 .

Figure 20:  Reconstructed energy distributions for 3 (left), 7 (middle) and 11
GeV (right) pion data samples collected at the PS. The distributions are
fitted with a double sided Crystal Ball function. The variance of the Gaussian
part of the Crystal Ball function is used to estimate the resolution of the
reconstructed energy.

The reconstructed energy distributions of 20, 40 and 60 GeV pion data samples
collected at SPS are shown in Fig. 21 .

Figure 21:  Reconstructed energy distributions for 20 (left), 40 (middle) and
60 GeV (right) pion data samples collected at SPS. The distributions are
fitted with a double-sided Crystal Ball function. The variance of the Gaussian
part of the Crystal Ball function is used to estimate the resolution of the
reconstructed energy.

After fitting the reconstructed energy distribution from 3 to 80 GeV, the mean
value and standard deviation of the Gaussian function are taken as the
reconstructed energy and its resolution respectively. In Fig. 22, the energy
linearity (left) and resolution results (right) are shown using both PS and
SPS data.

The same procedure is applied to the SPS sample only. Similar results as the
one obtained with both PS and SPS beamlines are obtained as can be shown in
Fig. 23. More importantly, these SPS results are similar to those obtained in
2012 [6]. This confirms the robustness of the SDHCAL prototype over time.

|
---|---
Figure 22: Mean reconstructed energy of pion showers as a function of the beam energy as well as relative deviation of the pion mean reconstructed energy with respect to the beam energy (left) and resolution of the reconstructed hadron energy as a function of the beam energy (right). Both statistical and systematic uncertainties are included in the error bars. Dashed line on the left plot indicates the ideal linearity response of the calorimeter.  |
---|---
Figure 23: Mean reconstructed energy for pion showers as a function of the
beam energy as well as relative deviation of the pion mean reconstructed
energy with respect to the beam energy (left) and resolution of the
reconstructed hadron energy as a function of the beam energy (right). Both
statistical and systematic uncertainties are included in the error bars.
Dashed line on the left plot indicates the ideal linearity response of the
calorimeter.

##  5 Uncertainties estimation

The linearity and energy resolution results presented in section. 4.1 include
statistical and systematic uncertainties. We present here after the main
contributions to the systematic uncertainties:

  * •

For the reconstructed energy of all energy points, a double sided Crystal Ball
fit function and a Gaussian fit function are used. The difference of fitting
results obtained from these two fit functions are considered as the value of
systematic uncertainties associated to the fit of the reconstructed energy.

  * •

For the muon rejection, using all energy points data samples of PS, the
meanRadius varied by an arbitrary 5% in both directions with respect to the
nominal values. The maximum deviation with respect to the nominal value is
used as an estimate of the systematic uncertainties due to a residual muon
contamination.

  * •

For the electron rejection using the BDT method, the BDT cut value is changed
from -0.05 to 0.05 with respect to the nominal values 0.0. The maximum
deviations are taken and added to the systematic uncertainties as an estimate
of the impact of residual electron contamination.

Although the statistical uncertainties are found to be negligible for almost
all the runs with respect to systematic uncertainties, their contributions as
well as the systematic uncertainties previously discussed are added
quadratically to obtain the final uncertainties. The results are summarised in
Tables 2 and 3. The uncertainty coming from the different fit functions is
found to be the main component of total systematic uncertainties.

Energy(GeV) | Beam data of PS and SPS
---|---
$3$ | $0.068\pm 0.011$
$4$ | $0.006\pm 0.017$
$5$ | $-0.013\pm 0.017$
$6$ | $-0.013\pm 0.019$
$7$ | $-0.016\pm 0.016$
$8$ | $-0.004\pm 0.021$
$9$ | $-0.006\pm 0.010$
$10$ | $-0.012\pm 0.007$
$11$ | $-0.001\pm 0.009$
$20$ | $0.009\pm 0.001$
$30$ | $0.032\pm 0.001$
$40$ | $0.021\pm 0.001$
$50$ | $0.019\pm 0.001$
$60$ | $-0.001\pm 0.001$
$70$ | $-0.010\pm 0.001$
$80$ | $-0.027\pm 0.001$
Table 2: List of $\frac{\Delta E}{E}$ observed and associated uncertainties for beam data in the energy range from 3 to 80 GeV. Energy(GeV) | Beam data of PS and SPS
---|---
$3$ | $0.282\pm 0.085$
$4$ | $0.281\pm 0.081$
$5$ | $0.279\pm 0.042$
$6$ | $0.266\pm 0.030$
$7$ | $0.251\pm 0.025$
$8$ | $0.244\pm 0.020$
$9$ | $0.244\pm 0.001$
$10$ | $0.236\pm 0.001$
$11$ | $0.224\pm 0.006$
$20$ | $0.160\pm 0.007$
$30$ | $0.130\pm 0.001$
$40$ | $0.117\pm 0.001$
$50$ | $0.103\pm 0.002$
$60$ | $0.092\pm 0.005$
$70$ | $0.081\pm 0.009$
$80$ | $0.067\pm 0.001$
Table 3: List of energy resolution observed and associated uncertainties for
beam data in the energy range from 3 to 80 GeV.

##  6 Conclusion

The data collected from the exposure of the SDHCAL prototype to pion beams in
both PS and SPS covering a large range of energy (3-80 GeV) are analyzed.
Rejection of muon and electron contamination is performed. For the latter a
BDT-based technique is applied. This technique allows the rejection of the
electron contamination without reducing the pion sample compared to the
analysis used in Ref. [6] where the electron contamination was reduced by
requiring the interaction to start showering after ten radiation lengths
($X_{0}$) leading to a loss of about half of the pion events. Energy of the
pions collected in both PS and SPS is then reconstructed following the
techniques developed in Ref. [6] and compared with those obtained with the SPS
data only and with those obtained with those of 2012 following a standard
selection analysis. The results show that good performances including
excellent linearity and energy resolution, are obtained over a large dynamic
range from 3 to 80 GeV.

##  7 Acknowledgements

This study was supported by the French ANR agency (DHCAL Grant) and the CNRS-
IN2P3; by the MCIN/AEI and the Programa Estatal de Fomento de la Investigación
Cientifica y Técnica de Excelencia Maria de Maeztu, grant MMDM-2015-0509; by
the National Natural Science Foundation of China (Grant No. 11961141006) and
the National Key Programmes for S&T Research and Development (Grant No.
2016YFA0400404); by the National Research Foundation of Korea, Grant Agreement
2019R1I1A3A01056616. The authors would also like to thank the CERN accelerator
staff for their precious help in preparing both the PS and the SPS beams.

## References

  * [1] G. Baulieu et al., Construction and commissioning of a technological prototype of a high-granularity semi-digital hadronic calorimeter, JINST 10 (2015) P10039.
  * [2] M. A. Thomson, Particle Flow Calorimetry and the PandoraPFA Algorithm, NIMA 611 25 (2009), arXiv:0907.3577
  * [3] V. L. Morgunov, Calorimetry design with energy-flow concept (imaging detector for high-energy physics), in Proc of Int. Conf. on Calorimetry (Calor02), (2002) Pasadena, 70.
  * [4] J. C. Brient and H. Videau, The calorimetry at the future e+e- linear collider, in Proc. of APS/DPF/DBP summer study on the future of particle physics, (2002) Snowmass, Colorado [hep-ex/0202004].
  * [5] F. Dulucp, C. de La Taille, G. Martin-Chassard, N. Seguin-Moreau, HARDROC: Readout chip for CALICE/EUDET Digital Hadronic Calorimeter, IEEE Nuclear Science Symposuim & Medical Imaging Conference, IEEE, 2010.
  * [6] CALICE collaboration, First results of the CALICE SDHCAL technological prototype, JINST 11 (2016) P04001.
  * [7] S. Agostinelli et al. GEANT4 - a simulation toolkit, NIMA  506 (2003) 250-303.
  * [8] CALICE collaboration, Resistive Plate Chamber Digitization in a Hadronic Shower Environment, JINST 11 (2016) P06014, arXiv:1604.04550.
  * [9] B. P. Roe, H. J. Yang, J. Zhu et al., Boosted decision trees as an alternative to artificial neural networks for particle identification, NIMA 543 (2004) 577-584.
  * [10] H. J. Yang, B. P. Roe, J. Zhu et al., Studies of boosted decision trees for MiniBooNE particle identification, NIMA 555 (2005) 370-385.
  * [11] CALICE Collaboration, Hadron selection using Boosted Decision Trees in the semi-digital hadronic calorimeter , https://inspirehep.net/record/1718039.
  * [12] A. Hoecker, P. Speckmayer, J. Stelzer, J. Therhaag, E. von Toerne and H. Voss, TMVA-Toolkit for multi data analysis,arXiv:physics/0703039.
  * [13] CALICE collaboration. "Tracking within Hadronic Showers in the CALICE SDHCAL prototype using a Hough Transform Technique." arXiv preprint arXiv:1702.08082 (2017).


11institutetext:  SimulaMet, Norway 22institutetext: Oslo Metropolitan
University, Norway 33institutetext: UIT The Arctic University of Norway
44institutetext: Department of Engineering Science, University of Oxford,
Oxford, UK
44email<EMAIL_ADDRESS>
# DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation

Nikhil Kumar Tomar 11 Debesh Jha 1133 Sharib Ali 44 Håvard D. Johansen 33 Dag
Johansen 33 Michael A. Riegler 11 Pål Halvorsen 1122

###### Abstract

Colonoscopy is the gold standard for examination and detection of colorectal
polyps. Localization and delineation of polyps can play a vital role in
treatment (e.g., surgical planning) and prognostic decision making. Polyp
segmentation can provide detailed boundary information for clinical analysis.
Convolutional neural networks have improved the performance in colonoscopy.
However, polyps usually possess various challenges, such as intra-and inter-
class variation and noise. While manual labeling for polyp assessment requires
time from experts and is prone to human error (e.g., missed lesions), an
automated, accurate, and fast segmentation can improve the quality of
delineated lesion boundaries and reduce missed rate. The Endotect challenge
provides an opportunity to benchmark computer vision methods by training on
the publicly available Hyperkvasir and testing on a separate unseen dataset.
In this paper, we propose a novel architecture called “DDANet” based on a dual
decoder attention network. Our experiments demonstrate that the model trained
on the Kvasir-SEG dataset and tested on an unseen dataset achieves a dice
coefficient of 0.7874, mIoU of 0.7010, recall of 0.7987, and a precision of
0.8577, demonstrating the generalization ability of our model.

###### Keywords:

Polyp segmentation, Deep Learning, Convolutional neural network, Benchmarking

##  1 Introduction

Colorectal cancer is one of the leading causes of cancer. Colonoscopy is a
standard medical procedure for the surveillance examination and treatment.
Regular screening and removal of pre-cancerous lesions through colonoscopy is
essential for early cancer detection and prevention. Studies suggest that the
miss-rate of adenoma is between 6 to 27% [1].

The automatic segmentation of the suspected areas with lesions in colonoscopy
images can play a crucial role, and identifying each colon pixel can
significantly impact clinical settings. With the increase of publicly
available datasets, dominant methodology such as convolutional neural network,
improved hardware, and collaboration between computational and clinical
communities to tackle the problems in endoscopic imaging through computer
vision tasks is gaining momentum than ever before. An automatic polyp
detection or surveillance system can help to achieve low-cost design solutions
and save time of clinicians allowing them to use their time to look into more
severe cases.

In this respect, the Endotect challenge [8] offers three tasks, namely,
detection of Gastrointestinal (GI) tract images, efficient detection on the
same images, and automatic polyp segmentation. The detection and efficient
detection task are based on the HyperKvasir dataset [5], and the segmentation
is based on the Kvasir-SEG dataset [12]. Out of these three tasks, we
participated in the “segmentation task”, where the goal was to generate an
automatic segmentation of the polyps for the unseen dataset.

In this paper, we propose a novel deep learning architecture, called Dual
Decoder Attention Network (DDANet), for automatic polyp segmentation. It
follows an encoder-decoder scheme and incorporates a single encoder that is
shared by two parallel decoders, where the first decoder acts as a
segmentation network and the second decoder acts as an autoencoder network.
The autoencoder network helps to strengthen the feature maps in the encoder
network. It is used as an auxiliary task training, which is used to generate
an attention map. This attention map is used in each decoder to improve the
semantic representation of the feature maps. This, in turn, helps to improve
the performance of the entire network. The proposed DDANet is fed with an RGB
input image,where it predicts the segmentation mask and the reconstructed
grayscale image. The architecture is efficient in terms of Frame per Second
(FPS) and also has a decent evaluation score. These metrics are the
requirement for the real-world settings toward developing a Computer Aided
Diagnosis (CADx) system.

##  2 Related Work

Automatic polyp segmentation task is a well-defined computer vision problem.
Recently, there have been several competitions [4, 3, 2, 10] and individual
efforts [7, 13, 11, 6, 9] toward building a CADx system for the polyp
segmentation. With these competitions and individual efforts, polyp
segmentation is becoming more and more mature. However, comparing models and
results of the many individual approaches is difficult due to the use of
diverse (often publicly non-available) datasets and different hardware. In
this respect, competitions provide an opportunity to benchmark and compare the
designed methods with other competitors’ on the same dataset. Moreover, the
evaluation metrics are independently calculated by the organizers, including
the ranking decision of each team.

The competitions can help us to define the strengths and weaknesses of each
method. It also provides us with an opportunity to disseminate methods and
discuss the results collectively in the same space. Through this year’s
Endotect challenge, we provide a novel solution to develop more efficient
algorithms that can be useful to build an automatic polyp segmentation system.
Our architecture is composed of an autoencoder branch in addition to the
segmentation branch, which is different from other encoder-decoder based
network (for example, UNet [14], ResUNet++ [13], DoubleUNet [11]). The benefit
of incorporating autoencoder in the network can be seen from the quantitative
and qualitative results.

Figure 1: DDANet architecture and its components.

##  3 DDANet

In this section, we will first describe each component of our DDANet and then
detail the overall proposed DDANet architecture.

###  3.1 Residual block

As the network depth increases, the performance also increases to a certain
limit as the gradients can be effectively calculated. However, after a certain
depth, the performance of the model may be impacted due to the vanishing or
exploding gradients as the gradients become either zero or too large. By
introducing a skip-connection in residual learning, the problem of the
vanishing or exploding gradients has been solved. Our residual block (see
Figure 1a) consists of two $3\times 3$ convolutions, each followed by a batch
normalization and a Rectified linear unit (ReLU) activation function. The
residual learning introduces a shortcut connection or identity mapping, which
connects the input with the residual block’s output. The identity mapping
tries to learn an identity function since the input is directly passed to the
output. It also helps in a better flow of the gradients during the
backpropagation.

###  3.2 Squeeze and Excitation block

A Convolutional Neural Network (CNN) is used to extract features from an image
and then transform the image into a feature map. A problem with CNNs is that
they treat every feature channel as equally important. To overcome this
problem, we introduce a squeeze and excitation layer, which acts as a channel-
wise attention mechanism. It re-weights every feature channel accordingly to
create a more accurate feature map. In this way, the overall network becomes
more sensitive towards essential features that improve the network performance
significantly. The squeeze and excitation network mainly consists of two
steps. In the first step, the feature maps are compressed using the global
average pooling function to generate a compressed representation for the
feature maps. While, in the second step, a $2$-layered neural network is used,
where features are first reduced and then expanded. This generates a feature
vector, which is used to scale the feature channels.

###  3.3 The DDANet architecture

The proposed architecture named DDANet follows an encoder-decoder design
similar to ResUNet++ [13]. The DDANet combines the strength of the residual
learning and the squeeze and excitation network. The proposed DDANet is a
fully convolutional network that consists of a single encoder shared by dual
decoders. The encoder network consists of a $4$ encoder block, whereas each
decoder network also consists of $4$ decoder block (see Figure 1d).

The RGB input image is first fed into the encoder network (see Figure 1b),
which encodes it into an abstract feature representation while gradually
downsampling it. The output of the encoder network is fed to both decoders
(see Figure 1c), where it is followed by a $4\times 4$ transpose convolution
that doubles its spatial dimensions. After that, the image is concatenated
with an appropriate feature maps from the encoder network using the skip
connection. These skip connections fetch the features from earlier layers at
their original resolution, which increases their feature representation
strength. The skip connections also act as an alternative path for the
gradient flow and are often beneficial for model convergence.

Two residual blocks are then used to learn the necessary feature required by
the network during back-propagation. The output of the second decoder block
(autoencoder branch) follows a $1\times 1$ convolution and a sigmoid
activation function to generates an attention map. This attention map is
multiplied by the output of the first decoder block (segmentation branch),
which acts as an input for the next decoder block in the segmentation branch.
The final decoder block’s output is passed through a $1\times 1$ convolution
and a sigmoid activation function, where the first decoder outputs a
segmentation mask, and the second outputs the reconstructed grayscale image.

##  4 Experimental Setup

In this section, we present the implementation details and datasets used in
this work.

###  4.1 Implementation Details

The proposed DDANet architecture is implemented in the PyTorch $1.6$
framework111https://github.com/nikhilroxtomar/DDANet. For training the DDANet,
we used an NVIDIA DGX-2 machine that uses an Nvidia V100 Tensor Core
GPUs.During training, we have used an input image resolution of $512\times
512$. We use a combination of binary cross-entropy and dice loss for
calculating the loss between the predicted masks and the ground-truth masks.
We have used binary cross-entropy in the case of predicting the grayscale
image. An Adam optimizer was used with a learning rate of 1e-4. The models
were trained for 200 epochs.

###  4.2 Datasets

The Kvasir-SEG [12] dataset was used for training the model. We have used 88%
of the dataset for training and the remaining 12% images for development-test-
set. Kvasir-SEG consists of 1000 polyp images, ground truth segmentation
masks, and bounding boxes. A separate test dataset with 200 images was
provided for prediction. However, the ground truth for this dataset was not
provided by the organizers. The exact number of images used for the training
and testing can also be found in our GitHub repository. More details about the
dataset and the baseline results on it can be found in [12].

##  5 Results

Table 1: Quantitative results on Kvasir-SEG and unseen (Challenge) dataset.
Dataset | Method | DSC | mIOU | Recall | Precision | FPS
---|---|---|---|---|---|---
Kvasir-SEG | DDANet | 0.8576 | 0.7800 | 0.8880 | 0.8643 | 69.59
Unseen (Challenge) | DDANet | 0.7874 | 0.7010 | 0.7987 | 0.8577 | 70.23
Figure 2: Qualitative results of the DDANet on the Kvasir-SEG test dataset.
The blue line divides the segmentation and the reconstruction part. Columns 4
and 5 show the reconstruction part that was used in the DDANet as an auxiliary
task.

Table 1 shows the results of the DDANet trained and validated on Kvasir-SEG.
Additionally, evaluation scores on the test dataset can also be found here.
The evaluation metrics for the challenge was Dice Coefficient (DSC). However,
we have also calculated other commonly used metrics such as mean Intersection
over Union (mIoU), recall, precision, and FPS. The DDANet obtained a DSC of
0.8576, a mIoU of 0.7800, a recall of 0.8880, and a precision of 0.8643. All
the metrics suggest that our method performs quite well on the Kvasir-SEG
dataset. When we compare the results with our previous results [13, 12], where
the DSC values were 0.8133, and 0.7877, DDANet achieves a higher DSC of
0.8576. However, we can not compare directly with this work with our previous
works as a different train-test split of the dataset is used.

Figure 2 shows the qualitative results of the DDANet on Kvasir-SEG. The figure
shows that the proposed DDANet is able to segment both larger and smaller
polyps. However, the figure also shows the challenges in identifying the flat
polyps, which is one of the open issues in the field of development of CADx
systems for colonoscopy. From the quantitative results on development and
unseen test dataset, we can say that the proposed method is comprehensive in
producing reliable segmentation output.

##  6 Discussion

The qualitative results (see Figure 2) show that the proposed model was able
to segment polyps ranging from large to small (Figure 2), but still,
challenges remain within some polyps (for example, flat or sessile). We can
also see a nearly perfect reconstruction of the grayscale image. In the
future, we would like to use image super-resolution instead of just a
grayscale image reconstruction.

From all the results, we can see that our method achieves high precision and
recall evaluation scores on both the Kvasir-SEG validation dataset and on the
unseen test dataset (see Table 1). Additionally, we also achieved a DSC of
0.7874 on the unseen dataset. Thus, high DSC, recall, and precision results
validate our proposed method. Moreover, our approach is quite fast with an
average FPS of 70.23. Thus, the results show that our method can identify
polyps in real-time.

##  7 Conclusion

The Endotect challenge [8] aims to benchmark various computer-vision
approaches on the HyperKvasir dataset containing GI images and videos. Here,
we have proposed the DDANet architecture for automatic polyp segmentation, and
the proposed architecture provides good results in the segmentation task. We
have obtained a high precision, recall, DSC, mIoU, and FPS. However, there are
large rooms for improvements. We intend to further improve the architecture by
applying post-processing and analyzing the optimal parameters in the future.

## Acknowledgements

This work is funded in part by the Research Council of Norway, project number
263248 (Privaton) and project number 282315 (AutoCap). We performed all
computations in this paper on equipment provided by the Experimental
Infrastructure for Exploration of Exascale Computing ($eX^{3}$), which is
financially supported by the Research Council of Norway under contract 270053.

## References

  * Ahn et al. [2012] Ahn, S.B., Han, D.S., Bae, J.H., Byun, T.J., Kim, J.P., Eun, C.S.: The miss rate for colorectal adenoma determined by quality-adjusted, back-to-back colonoscopies.  Gut and liver 6(1), 64 (2012)
  * Ali et al. [2020a] Ali, S., Dmitrieva, M., Ghatwary, N., Bano, S., Polat, G., Temizel, A., Krenzer, A., Hekalo, A., Guo, Y.B., Matuszewski, B., et al.: A translational pathway of deep learning methods in gastrointestinal endoscopy.  arXiv preprint arXiv:2010.06034 (2020a)
  * Ali et al. [2020b] Ali, S., Zhou, F., Braden, B., Bailey, A., Yang, S., Cheng, G., Zhang, P., Li, X., Kayser, M., Soberanis-Mukul, R.D., et al.: An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy.  Scientific reports 10(1), 1–15 (2020b)
  * Bernal et al. [2017] Bernal, J., Tajkbaksh, N., Sánchez, F.J., Matuszewski, B.J., Chen, H., Yu, L., Angermann, Q., Romain, O., Rustad, B., Balasingham, I., et al.: Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge.  IEEE transactions on medical imaging 36(6), 1231–1249 (2017)
  * Borgli et al. [2020] Borgli, H., Thambawita, V., Smedsrud, P.H., Hicks, S., Jha, D., Eskeland, S.L., Randel, K.R., Pogorelov, K., Lux, M., Nguyen, D.T.D., Johansen, D., Griwodz, C., Stensland, H.K., Garcia-Ceja, E., Schmidt, P.T., Hammer, H.L., Riegler, M.A., Halvorsen, P., de Lange, T.: HyperKvasir, a Comprehensive Multi-Class Image and Video Dataset for Gastrointestinal Endoscopy.  Springer Nature Scientific Data 7, Article no. 283 (2020)
  * Guo and Matuszewski [2019] Guo, Y.B., Matuszewski, B.: Giana polyp segmentation with fully convolutional dilation neural networks.  In: Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. pp. 632–641 (2019)
  * Guo et al. [2020] Guo, Y., Bernal, J., J Matuszewski, B.: Polyp Segmentation with Fully Convolutional Deep Neural Networks—Extended Evaluation Study.  Journal of Imaging 6(7), 69 (2020)
  * Hicks et al. [2020] Hicks, S.A., Jha, D., Thambawita, V., Halvorsen, P., Hammer, H., Riegler, M.A.: EndoTect: A Competition on Automatic Disease Detection in the Gastrointestinal Tract.  In: ICPR 2020 Workshops and Challenges. LNCS, Springer (2020)
  * Jha et al. [2020a] Jha, D., Ali, S., Johansen, H.D., Johansen, D., Rittscher, J., Riegler, M.A., Halvorsen, P.: Real-Time Polyp Detection, Localisation and Segmentation in Colonoscopy Using Deep Learning.  arXiv preprint arXiv:2006.11392 (2020a)
  * Jha et al. [2020b] Jha, D., Hicks, S.A., Emanuelsen, K., Johansen, H., Johansen, D., de Lange, T., Riegler, M., Halvorsen, P.: Medico Multimedia Task at MediaEval 2020:Automatic Polyp Segmentation.  In: Proceedings of MediaEval CEUR Workshop (2020b)
  * Jha et al. [2020c] Jha, D., Riegler, M., Johansen, D., Halvorsen, P., Johansen, H.: DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation.  In: Proceedings of the International Symposium on Computer Based Medical Systems (CBMS) (2020c)
  * Jha et al. [2020d] Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., de Lange, T., Johansen, D., Johansen, H.D.: Kvasir-SEG: A Segmented Polyp Dataset.  In: Proceedings of the International Conference on Multimedia Modeling (MMM). pp. 451–462 (2020d)
  * Jha et al. [2019] Jha, D., Smedsrud, P.H., Riegler, M.A., Johansen, D., De Lange, T., Halvorsen, P., Johansen, H.D.: ResUNet++: An Advanced Architecture for Medical Image Segmentation.  In: Proceedings of the International Symposium on Multimedia (ISM). pp. 225–230 (2019)
  * Ronneberger et al. [2015] Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation.  In: Proceedings of International Conference on Medical image computing and computer-assisted intervention. pp. 234–241 (2015)

  *[GI]: Gastrointestinal
  *[DDANet]: Dual Decoder Attention Network
  *[FPS]: Frame per Second
  *[CADx]: Computer Aided Diagnosis
  *[CNN]: Convolutional Neural Network
  *[CNNs]: Convolutional Neural Network
  *[DSC]: Dice Coefficient
  *[mIoU]: mean Intersection over Union