Multi-Task Learning Based on Log Dynamic Loss Weighting for Sex
Classification and Age Estimation on Panoramic Radiographs
Igor Prado, David Lima, Julian Liang, Ana Hougaz, Bernardo Peters and Luciano Oliveira
Intelligent Vision Research Lab, Federal University of Bahia, Brazil
Keywords:
Multi-Task Learning, Panoramic Radiographs, Sex Classification, Age Estimation.
Abstract:
This paper introduces a multi-task learning (MTL) approach for simultaneous sex classification and age esti-
mation in panoramic radiographs, aligning with the tasks pertinent to forensic dentistry. For that, we dynam-
ically optimize the logarithm of the task-specific weights during the loss training. Our results demonstrate
the superior performance of our proposed MTL network compared to the individual task-based networks, par-
ticularly evident across a diverse data set comprising 7,666 images, spanning ages from 1 to 90 years and
encompassing significant sex variability. Our network achieved an F1-score of 90.37%±0.54 and a mean ab-
solute error of 5.66±0.22 through a cross-validation assessment procedure, which resulted in a gain of 1.69
percentage points and 1.15 years with respect to the individual sex classification and age estimation proce-
dures. To the best of our knowledge, it is the first successful MTL-based network for these two tasks.
1 INTRODUCTION
In forensic dentistry, specialists provide their profi-
ciency in examining dental records, bite mark anal-
yses, and dental anatomical features to offer support
to law enforcement agencies and the judicial system.
These experts undertake the task of aligning dental
records with unidentifiable remains and scrutinizing
bite marks found on victims or objects. Notably, the
application of dental radiographs has emerged as an
important resource, offering insights, particularly in
scenarios involving mass disasters or when conven-
tional identification approaches prove unviable.
In particular, dental radiographs of panoramic
type represent a minimally invasive yet highly infor-
mative method for extracting pertinent details about
an individual. From a single panoramic image, essen-
tial information such as sex and age can be deduced,
significantly refining the process of narrowing down
potential matches and constructing a comprehensive
biological profile for unidentified individuals. Recent
advancements have witnessed the integration of deep
neural networks for the purpose of automating the ex-
traction of information from dental radiographs(Jader
et al., 2018; Tuzoff et al., 2019; Silva et al., 2020;
Pinheiro et al., 2021; Silva et al., 2023; Hougaz et al.,
2023; Liang et al., 2023). However, there remains an
unaddressed challenge of effectively integrating the
processes related to sex and age estimation within a
unified framework, a concern that needs careful inves-
tigation and methodological development in the field.
Prevailing approaches often address those tasks
as individual networks (Rajee and Mythili, 2021;
Ke et al., 2020; Ili
´
c et al., 2019; Hougaz et al.,
2023; Milo
ˇ
sevi
´
c et al., 2022a; Vila-Blanco et al.,
2020; Liang et al., 2023). However, integrating ma-
chine learning techniques, with emphasis on multi-
task learning (MTL) models, can substantially en-
hance operational efficiency by speeding up training
and prediction, accuracy, and resource utilization.
Age and sex estimation from panoramic images
pose certain challenges. While evaluating young
adults with complete dentition simplifies the task, as-
sessing the age and sex of younger and elderly in-
dividuals becomes notably intricate, demanding spe-
cialized expertise. This complexity arises due to the
absence of dimorphic mandibular features in younger
individuals, which hinders clear sex identification,
and in elderly individuals (Badran et al., 2015), where
morphological characteristics used for distinguishing
sex and age groups tend to lose reliability over time.
Figure 1 illustrates some examples of radiographs
used for sex classification and age estimation: Radio-
graphs (a), (c), (e), and (g) belong to females, and (b),
(d), (f), and (h) to males. These radiographs are also
categorized by age groups: (a) and (b) represent indi-
viduals with ages spanning from 0 to 20 years, (c) and
(d) are from those aged 21 to 40 years, (e) and (f) to
Prado, I., Lima, D., Liang, J., Hougaz, A., Peters, B. and Oliveira, L.
Multi-Task Learning Based on Log Dynamic Loss Weighting for Sex Classification and Age Estimation on Panoramic Radiographs.
DOI: 10.5220/0012317100003660
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024) - Volume 2: VISAPP, pages
385-392
ISBN: 978-989-758-679-8; ISSN: 2184-4321
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
385

Table 1: Comparison of works on sex classification and age estimation using panoramic radiographs.
Reference Year Task Pre-processing Classifier Data set Performance
(Ili
´
c et al., 2019) 2019 Sex Masking VGG-16 4,155 (images) ACC = 94.3%
(Ke et al., 2020) 2020 Sex Image adjustments +
GradCAM
VGG-16 19,976 (images) ACC = 94.6%± 0.58
(Rajee and Mythili,
2021)
2021 Sex Image adjustments ResNet-50 1,000 (images) F1
∼
=
66.55% ACC =
98.27%
(Hougaz et al., 2023) 2023 Sex GradCAM EfficientNetV2-Large 16,824 (images) F1 = 91.43% ± 0.67
(Vila-Blanco et al.,
2020)
2020 Age Image adjustments DASNet 2,289 (images) MAE = 2.84 ±3.75
(Milo
ˇ
sevi
´
c et al., 2022a) 2022 Age Image adjustments +
Augmentation
VGG-16 4,035 (images) MAE = 3.96
(Liang et al., 2023) 2023 Age Image adjustments EfficientNet-B7 7,666 (images) MAE = 4.46
(Vila-Blanco et al.,
2022)
2022 Age and Sex Bounding box for
tooth detection
Faster R-CNN architecture
with ResNet-50
1,746 (images) F1
∼
=
91.80% ACC = 91.82%
and MAE = 0.97
(Milo
ˇ
sevi
´
c et al., 2022b) 2022 Age and Sex Bounding box for
tooth detection
VGG-16 86,495 (teeth) F1=74.90% ACC = 76.41%
and MAE = 4.94
Ours 2023 Age and Sex Image adjustments EfficientNet V2-L 7,666 (images) F1 = 90.37% ± 0.54 and
MAE = 5.66 ± 0.22
(a) (b)
(c) (d)
(e) (f)
(g) (h)
Figure 1: Examples of radiographs distributed by sex and
age. Radiographs (a), (c), (e), and (g) belong to females,
while (b), (d), (f), and (h) belong to males. These radio-
graphs are also categorized by age groups: (a) and (b) rep-
resent individuals aged 0 to 20 years, (c) and (d) are from
those aged 21 to 40 years, (e) and (f) to 41 to 60 years age
group, and (g) and (h) represent individuals aged from 61
to 80 years. Over time, morphological features that can be
used to distinguish between different sex and age groups
tend to become less reliable indicators.
41 to 60 years age group, and (g) and (h) individuals
aged from 61 to 80 years. While individuals get older,
morphological features that can be used to distinguish
between different sex and age groups tend to become
less reliable indicators.
Taking all of these factors into account, we in-
troduce in this paper a novel method to dynamically
weigh the parameters used for each task-based loss in
an end-to-end deep network. The goal is to leverage
multi-task learning for concurrent sex classification
and age estimation. It is noteworthy that the nature
of the tasks relies on classification and regression op-
erations, which are concurrent with each other.
1.1 Related Work
Table 1 provides a comprehensive overview of the
studies addressing the challenges of sex classifica-
tion and/or age estimation from panoramic radio-
graphs. The selected columns aim to emphasize key
attributes (reference, year, task, pre-processing, clas-
sifier, data set, and performance), facilitating an at-a-
glance benchmark among these works.
Sex Classification Only. Some studies have devel-
oped classification models for sex classification based
on dental images. Ili
´
c et al. (2019) presented their
results with a distribution across age groups, show-
casing that the age group between 40 and 50 exhib-
ited the highest accuracy, while the age group over
80 displayed the lowest performance. Wenchi et al.
(2020) enhanced the accuracy of VGG16 by incorpo-
rating a multiple feature fusion (MFF) model; this ap-
proach yielded an accuracy of 94.6% ±0.58 through a
modified cross-validation technique on a data set con-
sisting of 19,976 images. Rajee et al. (2021) intro-
duced a gradient-based recursive threshold segmen-
tation (GBRTS) method for segmenting dental radio-
graphic images, which contributed to achieving an ac-
curacy of 98.27% within the age range of 20 to 60
years, even with a considerably smaller data set of
1,000 images. Hougaz et al. (2023) explored Effi-
cientNet architectures to perform sex classification,
having the EfficientNet V2-L and EfficientNet B0 as
the best models via cross-validation, resulting in an
accuracy of 91.43% ± 0.67 and 91.30% ± 0.47, re-
spectively, over a data set containing 16,824 images.
Only Hougaz et al. make a public data set available
for the academic community.
VISAPP 2024 - 19th International Conference on Computer Vision Theory and Applications
386

(III) Multi-task framework
EfficientNet
V2-S
Age
Sex
Raw image
MSE Loss
Cross-entropy Loss
EfficientNet
Architectures
EfficientNetV2 Large
EfficientNetV2 Small
EfficientNet B7
EfficientNet B0
5-fold Cross-validation
Best evaluated model
for sex classification
Best evaluated model
for age estimation
Lowest loss
Lowest loss
EfficientNet V2-S
EfficientNet V2-L
(II) Model Evaluation
(I) Training
7,666 images
Dental Panoramic
Radiograph Data set
6 folds of ~1,278 images
Testing
Training
(6,390)
5-fold Cross-validation
Validation
Training
Figure 2: A visual representation of each step taken when creating the multi-task framework. (I) Data division and training
method (5-fold Cross-validation), (II) evaluation of four different EfficientNet models, and (III) MTL-based network and loss
function optimizations.
Age Estimation Only. Vila-Blanco et al. (2020) in-
troduced the DASNet architecture; despite incorpo-
rating sex classification in the proposed architecture,
the primary objective of the network was to share
resources between two backbones to ultimately en-
hance age estimation; the mean absolute error (MAE)
achieved was 2.84 years, over a small data set contain-
ing 2,289 images. Milo
ˇ
sevi
´
c et al. (2022) conducted
six experiments using different pre-trained feature
extractors and achieved the best performance with
VGG16, using a data set comprising 4,035 images;
the final result was an MAE of 3.96 years. Liang et
al. (2023) explored the use of the EfficientNet, Con-
vNeXt, and ViT architectures, employing the largest
publicly available data set comprising 7,666 images
spanning age ranges from 1 to 90 years; the MAE
achieved was 4.46 years using the EfficientNet-B7 ar-
chitecture. The only available data set is provided in
(Liang et al., 2023), which we used for performance
assessment in our work.
Sex Classification and Age Estimation. Vila-
Blanco et al. (2022) proposed tooth detection us-
ing a rotated R-CNN to extract oriented bounding
boxes for each tooth. Subsequently, the image fea-
tures within these tooth boxes feed a second CNN
module designed to generate age and sex probabil-
ity distributions per tooth; an uncertainty-aware pol-
icy is employed to aggregate these estimated distribu-
tions. The proposed approach achieved an MAE of
0.97 years among individuals aged 5 to 25 years and
an accuracy of 91.8% in sex classification in the age
group ranging from 16 to 60 years over 1,746 images.
Although age estimation presents the smallest error
compared to other works, it focused only on the age
range of young individuals, and the MTL approach
did not outperform the single classifiers. Milo
ˇ
sevi
´
c
et al. (2022a) evaluated different single-task mod-
els for both sex classification and age estimation, ob-
taining an accuracy of 76.41% and an MAE of 4.94,
respectively; they experimented with the possibility
of a joint model using MTL, but tests yielded results
that underperformed in comparison to the single-task
models. None of these works provide a public data
set for benchmark purposes.
1.2 Contributions
To date, the challenge of concurrently classifying sex
and estimating age on dental panoramic radiographs
through MTL has remained largely unaddressed. In
response, this study introduces a novel method involv-
ing dynamic log weighting of each task’s loss to op-
timize an MTL-based end-to-end deep network. The
core principle is to optimize parameters that enhance
the mean F1-score of classification while minimizing
the mean MAE of regression (age estimation). Our
findings demonstrate the efficacy of this approach,
outperforming the individual networks employed for
each task, which has not been achieved by any other
work before.
Multi-Task Learning Based on Log Dynamic Loss Weighting for Sex Classification and Age Estimation on Panoramic Radiographs
387

2 OUR PROPOSED APPROACH
Constructing our MTL-based network involved three
steps, depicted in Fig. 2. Initially, we gathered a pub-
licly available data set from (Liang et al., 2023), em-
ploying the same methodological protocol for perfor-
mance assessment. Subsequently, these images were
partitioned into six folds, with each one encompass-
ing around 1,278 images, totaling 7,666 images. One
fold was earmarked for testing purposes, while the
remaining five were allocated for training. Training
procedures were conducted utilizing a 5-fold cross-
validation methodology (see Fig. 2-I).
Four different EfficientNet models were evaluated
for sex classification and age estimation, selecting
the best-performing model for each task based on
its loss. In our experiments, the V2-S architecture
yielded the best results for sex classification, while its
larger counterpart was the best-performing model for
age estimation (see Fig. 2-II). These experiments are
detailed in Section 3. Finally, our MTL-based end-to-
end network was comprised of an EfficientNet V2-S
and two neurons in the last layer, ultimately trained
by considering the integration of two types of losses:
mean square error (MSE) and cross-entropy (see Fig.
2-III).
2.1 Dynamic Log-Loss Weighting
In traditional MTL approaches to machine learning
problems, each loss function corresponding to each
task is attributed a weight, and the resulting products
are summed to generate the final MTL loss. How-
ever, the manual tuning of weights constitutes a time
and resource-consuming task, depending on the pre-
cise predefined increments in the search process and
the number of different tasks. In this context, arises
the need for a dynamic algorithm capable of adjusting
the relative weights for each loss according to their
performance, measured using the relevant metrics.
A novel approach to this problem, although in
the context of scene understanding, was described in
(Cipolla et al., 2018), where an additional learnable
parameter σ is introduced to represent task-dependent
(homoscedastic) uncertainty, a type of uncertainty
correlated to the task’s nature, and thus not necessar-
ily avoidable through increasing amount of training
data. In the case where the tasks are sex classifica-
tion and age estimation, based on a training data set
of dental radiographic images, this uncertainty mea-
sure can represent the randomness linked to external
factors, such as patient’s genetics and lifestyle, which
can cause the existence of patients with different sex
or significantly different age and similar radiographic
images in the data set.
For regression tasks, given a model f with a set of
weights W, we define, as in (Cipolla et al., 2018), the
likelihood p(y|f
W
(x)) of input image x having label y
as a normal probability distribution on the y variable,
with mean equal to the model’s output, f
W
(x) and
standard deviation equal to the task’s homoscedastic
uncertainty parameter σ:
p(y|f
W
(x)) ∼ N (f
W
(x), σ
2
). (1)
Therefore, we have that the log-likelihood is given
by
log p(y|f
W
(x)) = −
1
2σ
2
(y−f
W
(x))
2
−logσ+ε, (2)
where ε is some constant term independent of
the model parameters, the images, and the la-
bels. It follows that the expected value of this
log-likelihood over a batch of n random samples
(x
1
, y
1
), . . . , (x
n
, y
n
), consisting each of an image and
a label, is simply given by
E[log p(y|f
W
(x))] = −
1
2σ
2
·
1
n
n
∑
i=1
(y
i
− f
W
(x
i
))
2
!
− logσ + ε, (3)
where
1
n
∑
n
i=0
(y
i
− f
W
(x))
2
is exactly the MSE.
In a similar manner, the log-likelihood relative
to a classification task is given by a scaled version
of the logSoftmax of the activations obtained from
the last layer of the network. This scaled version
uses the task’s own uncertainty parameter σ to en-
code the classifier’s confidence in its predictions. In-
deed, we have a multivariate probability distribution
p(y|f
W
(x)) which can be written as
p(y|f
W
(x)) = log Softmax

1
σ
2
f
W
(x)

, (4)
where an entry i corresponds to the probability p(y =
i|f
W
(x)) that image x belongs to the i-th class. There-
fore, as σ → ∞, the maximum difference between the
entries of the probability vector p(y|f
W
(x)) tend to 0,
which represents that the classifier is completely un-
certain of its predictions. Similarly, as σ → 0, each
probability vector tends to be a one-hot vector repre-
senting maximum confidence.
Introducing the approximation
1
σ
∑
i
exp(
1
σ
2
f
W
i
(x)) ≈

∑
i
exp(f
W
i
(x))

1
σ
2
(Cipolla
VISAPP 2024 - 19th International Conference on Computer Vision Theory and Applications
388

et al., 2018) which holds when σ → 1, we have that
log p(y = i|f
W
(x)) = log
exp(
1
σ
2
f
W
i
(x))
∑
j
exp(
1
σ
2
f
W
j
(x))
≈ log

exp(f
W
i
(x))

1
σ
2
σ

∑
j
exp(f
W
j
(x))

1
σ
2
=
1
σ
2
log
exp(f
W
i
(x))
∑
j
exp(f
W
j
(x))
− logσ (5)
and therefore
log p(y = i|f
W
(x)) ≈
1
σ
2
log
exp(f
W
i
(x))

∑
j
exp(f
W
i
(x))

− logσ.
(6)
From Eqs. 5 and 6, it is noteworthy that by repeating
this process in a batched manner, the first term of the
resulting sum will be the cross-entropy loss, assuming
as ground truth the sample belongs to the i-th class,
multiplied by a constant factor of
1
σ
2
.
Therefore, when learning simultaneously to clas-
sify sex and estimate age from dental radiographic im-
ages, the joint probability distribution, assuming in-
dependent, separate network outputs for both tasks,
will be equal to the product of the individual proba-
bility distributions. It follows that the negative log-
likelihood loss for this MTL process can be written
as
L(W, σ
1
, σ
2
) =
L
1
(W, σ
1
)
2σ
2
1
+
L
2
(W, σ
2
)
σ
2
2
+ log(σ
1
σ
2
), (7)
where L
1
denotes the loss for the regression task while
L
2
is the loss for the classification task.
In the experimental analysis, we observed that by
minimizing the loss from Eq. 7, it takes to a numer-
ically unstable process. Due to the discrete nature
of the steps taken in optimization algorithms, such
as stochastic gradient descent, it is possible for one
of the uncertainty parameters σ
1
, σ
2
to reach nega-
tive values, which escapes the domain of the loga-
rithm function and makes it impossible to calculate
the loss function in the next training step correctly.
Because of this instability, we have chosen to train
the logarithm of the parameters σ
1
, σ
2
, which differs
from (Cipolla et al., 2018), where the logarithm of the
variances σ
2
1
, σ
2
2
was trained instead. This solves the
domain problem, as the term log(σ
1
σ
2
) becomes a
sum of the new trainable parameters σ
′
1
= log σ
1
and
σ
′
2
= log σ
2
.
2.2 MTL-Based Network and Relevant
Hyperparameters
Our proposed MTL-based deep network is com-
prised of an EfficientNet architecture with weights
pre-trained on the ImageNet data set - and a fully-
connected layer on top, mapping the space of features
generated by the convolutional neural network to two
outputs between 0 and 1, according to Fig. 2-III. The
first output node represents the network prediction for
the sex attribute, which was chosen as 0 for female
and 1 for male. The second output node represents the
normalized estimation of the patient’s age, in which
the interval from 0 to 100 years was linearly mapped
to the interval from 0 to 1.
The EfficientNet model used as the backbone was
the V2-Small, selected for being the best model ac-
cording to experiments performed in each task sep-
arately, as described in Section 3.2. Additionally,
the fully-connected layer, working as a decoder for
the features generated by the EfficientNet network, is
comprised of 1280 nodes, using dropout with proba-
bility p = 0.2.
Finally, the initial value of the uncertainty param-
eters, σ
1
, σ
2
, were defined to satisfy
2σ
2
1
= σ
2
2
= 1 , (8)
in order to attribute equal initial values to the weights
of the individual losses in Eq. 7. Since the network is
trained considering the logarithm of the loss weights,
it implies the initial values of the uncertainty parame-
ters σ
′
1
= log σ
1
and σ
′
2
= log σ
2
were σ
′
1
= −0.5log 2
and σ
′
2
= 0.
3 EXPERIMENTAL ANALYSIS
3.1 Materials
Data Set. The data set comprises anonymized
panoramic radiographs labeled by age and sex, to-
taling 7,666 panoramic radiographs, available from
(Liang et al., 2023). Among these, there are 4,621
samples from women and 3,045 from men. These
images have non-standard dimensions due to being
captured with different equipment. Consequently, im-
ages were resized to match EfficientNet requirements.
Notably, this data set does not incorporate any artifi-
cial enhancements or synthetic images. This approach
was chosen to ensure real-world representation and
authenticity. Therefore, the panoramic radiographs
in the database exhibit a range of dental conditions,
including dental implant placements, cavities, peri-
odontitis, dental plaque, natural tooth loss, and jaw
Multi-Task Learning Based on Log Dynamic Loss Weighting for Sex Classification and Age Estimation on Panoramic Radiographs
389

Figure 3: Behavior of the uncertainty parameters during the
training with respect to the (normalized) mean F1-score and
MAE.
skeletal structure damage. This diversity enables a
comprehensive evaluation of the sex classification and
age estimation network’s performance across various
dental issues.
The average age of individuals in the data set is
32.47 years, categorizing our overall sample as adults
in terms of dental development. It is worth men-
tioning that age groups over 60 years old have a no-
tably smaller sample size, which should be consid-
ered during the result analysis. The age range within
the database spans from 1 to 90 years old, with a dis-
tribution of 60.12% women and 39.87% men. Con-
sequently, the sample distribution by age and sex is
non-uniform, with more images of younger individu-
als and a slight majority of women.
The ablation study considered four networks from
the EfficientNet series of deep convolutional archi-
tectures, aiming to obtain the one with the highest
performance to be used in our multi-task learning
framework. The rationale was to evaluate the lightest
and the heaviest of each version. The choice of the
EfficientNet was motivated by its known efficiency
(Huang et al., 2019), faster training times, and opti-
mized default hyperparameters, which contribute to
shortening the tuning process necessary to achieve
better generalization and more robust results over the
test data set. Additionally, using the lightest and heav-
iest models from both EfficientNet versions provides
a quantitative way to measure the impact of network
size and depth in the performance of the sex classifi-
cation and age estimation tasks.
3.2 Experimental Methodology
In order to evaluate our MTL-based network and
compare its performance with standard single-task
neural networks, a series of experiments were con-
ducted, consisting of separately training multiple
single-task architectures to classify sex and estimate
age of patients from the data set of 7,666 panoramic
radiographs. These experiments provided a quanti-
Table 2: Comparison of EfficientNet and EfficientNetV2
architectures on sex classification and age estimation in
single-task models. µ and σ denote the mean and standard
deviation obtained via a 5-fold cross-validation procedure.
Network Sex class. Age estim.
(µF1-score±σ) (µMAE±σ)
B0 87.15 ± 0.57 7.63 ±0.09
B7 85.83 ± 0.87 7.02 ±0.11
V2-S 88.68 ± 0.58 6.81 ±0.13
V2-L 87.60 ±0.15 6.34 ± 0.05
tative comparison between EfficientNet and Efficient-
NetV2, which aided in the selection of the appropriate
architecture for the MTL network. This methodology
resulted in 8 separate training processes, obtaining the
results shown in Table 2, which details the perfor-
mance metrics achieved, averaged across the cross-
validation process and presented with their respec-
tive standard deviations. According to this table, con-
cerning age estimation, it was observed that the B0
and B7 networks exhibited relatively lower efficiency
compared to the other architectures. In contrast, the
second-generation architectures within the Efficient-
Net series achieved the best performances. Specifi-
cally, V2-L achieved the lowest MAE of 6.34 years,
while V2-S obtained an MAE of 6.81 years. In the
case of gender classification networks, similar to age
estimation, second-generation architectures also out-
performed the others. However, the V2-S achieved
a higher F1-score of 88.6%, surpassing the V2-L,
which achieved an F1-score of 87.5%.
Finally, the best model generated through this
pipeline, according simultaneously to F1-score and
MAE over the test fold, was used for training our
MTL-based network. However, as seen in Table
2, the best models for each task were different, as
the EfficientNetV2-Small performed better in the sex
classification (with an F1-score of 88.6% ± 0.5%),
and the EfficientNetV2-Large obtained the best MAE,
of 6.43 ± 0.05, in the age estimation, with its smaller
variation coming second with a MAE of 6.81 ± 0.13.
Therefore, considering the performance and cost-
benefit analysis of computational resources, V2-S was
selected for training our network in the MTL-based
framework due to its superiority for the sex classifi-
cation task, while performing very close to the best
V2-L network in age estimation. The V2-S outper-
formed V2-L by 1.1% in F1-score, and it exhibited
only a 0.47-year difference in age estimation MAE
compared to V2-L, while the latter had 5.45 times
more training parameters than its smaller version.
VISAPP 2024 - 19th International Conference on Computer Vision Theory and Applications
390

Figure 4: Some of the best, (a), (b) and (c), and worst predictions, (d), (e) and (f), over the test set: (a): Sex: Female.
Prediction: Female. Age: 67 years. Prediction: 67.02 years. (b): Sex: Female. Prediction: Female. Age: 37 years.
Prediction: 37.98 years. (c): Sex: Female. Prediction: Female. Age: 34 years. Prediction: 34.04 years. (d): Sex: Female.
Prediction: Male. Age: 83 years. Prediction: 60.26 years. (e): Sex: Female. Prediction: Male. Age: 2 years. Prediction:
23.70 years. (f): Sex: Female. Prediction: Male. Age: 18 years. Prediction: 30.36 years.
3.3 Result Analysis
Our framework was trained for 80 epochs over the
data set of 7,666 panoramic radiographs, which are
the same conditions for the single-task experiments
described in Section 3.2. The results were an F1-
score of 90.37%±0.54, and a mean absolute error of
5.66±0.22. This score is the average over the 5 folds
defined in the cross-validation procedure, with stan-
dard deviation calculated relative to this mean.
As summarized in Table 2, our MTL framework
outperforms all selected single-task networks in both
tasks of sex classification and age estimation. This
demonstrates the effectiveness of sharing task-related
knowledge in the training process, the basis principle
of MTL, as well as the positive impact that introduc-
ing learnable weights for each task’s contribution to
the loss function had in the final performance. Fig-
ure 3 outlines this relationship in more detail, corre-
lating the mean F1-score and MAE over the valida-
tion set with the normalized mean uncertainty param-
eters σ
1
, σ
2
. The uncertainty parameters’ values at
each epoch were averaged over all 5 iterations of the
cross-validation procedure, and the resulting parame-
ters σ
(mean)
1
, σ
(mean)
2
were then divided by their sum.
It is also possible to observe from Fig. 3 that our
MTL-based network converges quickly, since most of
the performance metrics (F1-score and MAE) evolu-
tion was accomplished in the first few epochs.
3.4 Qualitative Results
Fig. 4 exhibits some of our proposed MTL-based net-
work’s best and worst predictions over the test data
set via cross-validation. The samples were chosen ac-
cording to simultaneous performance quality in both
tasks. According to this criteria, the best examples
were those simultaneously presenting the minimum
age difference between ground truth and prediction
and that classified the sex label correctly, while the
worst examples were those that maximized the age
difference and missed the correct sex label.
The images in Fig. 4 suggest the best network
performance in both sex classification and age esti-
mation tasks on panoramic radiographs of adults and
young adults, especially between the ages 30 and 40,
while underperforming in the extreme points of the
age range - children and seniors. It also suggests that
missing teeth negatively impact network accuracy and
age estimation error, as most negative examples reg-
ister the absence of some teeth.
4 CONCLUDING REMARKS
By learning tasks concurrently, the model can ben-
efit from the complementary nature of tasks, uncov-
ering hidden patterns and relationships that might be
missed in single-task learning scenarios. However, it
is not always trivial to benefit from MTL in scenarios
where the loss of a single task overshadows the loss of
the other tasks. If one task has a much higher loss than
others, the model might focus too much on minimiz-
ing that particular loss, thereby neglecting the other
tasks. An alternative in such cases is to search for
fixed weights that will result in better metrics for each
task. This option has two disadvantages: (i) It needs
some search to reach the optimal fixed weights, and
Multi-Task Learning Based on Log Dynamic Loss Weighting for Sex Classification and Age Estimation on Panoramic Radiographs
391

(ii) it does not consider that the losses vary consider-
ably during training. To address these challenges, we
proposed dynamically computing weights for losses
at every training stage. This method efficiently cir-
cumvents the need for intensive parameter searches
and adjusts weights in real time, reflecting the evolv-
ing nature of training losses. We conducted experi-
ments on a dental panoramic radiograph data set to
prove our method’s efficiency. Future work includes
experimenting with other strategies to integrate fea-
tures from the component tasks, synergistically.
ACKNOWLEDGMENT
Brazilian National Council for Scientific and Tech-
nological Development supported Luciano Oliveira
and Igor Prado under grants 308580/2021-4 and
118442/2023-6. Fundac¸
˜
ao de Apoio
`
a Pesquisa do
Estado da Bahia supported Bernardo Silva and David
Lima under grants BOL0569/2020 BOL1383/2023.
REFERENCES
Badran, D. H., Othman, D. A., Thnaibat, H. W., and M.,
A. W. (2015). Predictive accuracy of mandibular ra-
mus flexure as a morphologic indicator of sex dimor-
phism in jordanians. International Journal of Mor-
phology, 33(4):1248–1254.
Cipolla, R., Gal, Y., and Kendall, A. (2018). Multi-task
learning using uncertainty to weigh losses for scene
geometry and semantics. In 2018 IEEE/CVF Con-
ference on Computer Vision and Pattern Recognition,
pages 7482–7491.
Hougaz, A., Lima, D., Peters, B., Cury, P., and Oliveira,
L. (2023). Sex estimation on panoramic dental ra-
diographs: A methodological approach. In Anais do
XXIII Simp
´
osio Brasileiro de Computac¸
˜
ao Aplicada
`
a
Sa
´
ude, pages 115–125, Porto Alegre, RS, Brasil. SBC.
Huang, Y., Cheng, Y., Bapna, A., Firat, O., Chen, D., Chen,
M., Lee, H., Ngiam, J., Le, Q. V., Wu, Y., and Chen,
z. (2019). Gpipe: Efficient training of giant neu-
ral networks using pipeline parallelism. In Wallach,
H., Larochelle, H., Beygelzimer, A., d'Alch
´
e-Buc, F.,
Fox, E., and Garnett, R., editors, Advances in Neural
Information Processing Systems, volume 32, pages 1–
11. Curran Associates, Inc.
Ili
´
c, I., Vodanovi
´
c, M., and Suba
ˇ
si
´
c, M. (2019). Gender
estimation from panoramic dental x-ray images using
deep convolutional networks. In IEEE EUROCON
2019 -18th International Conference on Smart Tech-
nologies, pages 1–5.
Jader, G., Fontineli, J., Ruiz, M., Abdalla, K., Pithon, M.,
and Oliveira, L. (2018). Deep instance segmentation
of teeth in panoramic x-ray images. In 2018 31st SIB-
GRAPI Conference on Graphics, Patterns and Images
(SIBGRAPI), pages 400–407.
Ke, W., Fan, F., Liao, P., Lai, Y., Wu, Q., Du, W., Chen,
H., Deng, Z., and Zhang, Y. (2020). Biological Gen-
der Estimation from Panoramic Dental X-ray Images
Based on Multiple Feature Fusion Model. Sensing and
Imaging, 21(1):1–11.
Liang, J. S., Cury, P., and Oliveira, L. R. (2023). Revis-
iting age estimation on panoramic dental images. In
2023 36th SIBGRAPI Conference on Graphics, Pat-
terns and Images (SIBGRAPI).
Milo
ˇ
sevi
´
c, D., Vodanovi
´
c, M., Gali
´
c, I., and Suba
ˇ
si
´
c,
M. (2022a). Automated estimation of chronologi-
cal age from panoramic dental x-ray images using
deep learning. Expert Systems with Applications,
189(116038):1–23.
Milo
ˇ
sevi
´
c, D., Vodanovi
´
c, M., Gali
´
c, I., and Suba
ˇ
sI
´
c, M.
(2022b). A comprehensive exploration of neural net-
works for forensic analysis of adult single tooth x-ray
images. IEEE Access, 10:70980–71002.
Pinheiro, L., Silva, B., Sobrinho, B., Lima, F., Cury, P.,
and Oliveira, L. (2021). Numbering permanent and
deciduous teeth via deep instance segmentation in
panoramic x-rays. In Rittner, L., M.D., E. R. C.,
Lepore, N., Brieva, J., and Linguraru, M. G., editors,
17th International Symposium on Medical Informa-
tion Processing and Analysis, volume 12088, pages
1–10. International Society for Optics and Photonics,
SPIE.
Rajee, M. and Mythili, C. (2021). Gender classification on
digital dental x-ray images using deep convolutional
neural network. Biomedical Signal Processing and
Control, 69(102939):1–13.
Silva, B., Pinheiro, L., Oliveira, L., and Pithon, M. (2020).
A study on tooth segmentation and numbering using
end-to-end deep neural networks. In 2020 33rd SIB-
GRAPI Conference on Graphics, Patterns and Images
(SIBGRAPI), pages 164–171.
Silva, B. P. M., Pinheiro, L. B., Sobrinho, B. P. P., Lima,
F. P., Sobrinho, B. P. P., Abdalla Buzar Lima, K.,
Pithon, M. M., Cury, P. R., and Oliveira, L. R. d.
(2023). Boosting research on dental panoramic ra-
diographs: a challenging data set, baselines, and a
task central online platform for benchmark. Computer
Methods in Biomechanics and Biomedical Engineer-
ing: Imaging & Visualization, pages 1–21.
Tuzoff, D. V., Tuzova, L. N., Bornstein, M. M., Krasnov,
A. S., Kharchenko, M. A., Nikolenko, S. I., Svesh-
nikov, M. M., and Bednenko, G. B. (2019). Tooth de-
tection and numbering in panoramic radiographs us-
ing convolutional neural networks. Dento maxillo fa-
cial radiology, 48(4):1–10.
Vila-Blanco, N., Carreira, M. J., Varas-Quintana, P., Balsa-
Castro, C., and Tomas, I. (2020). Deep Neural Net-
works for Chronological Age Estimation from OPG
Images. IEEE Transactions on Medical Imaging,
39(7):2374–2384.
Vila-Blanco, N., Varas-Quintana, P.,
´
Angela Aneiros-
Ardao, Tom
´
as, I., and Carreira, M. J. (2022). Xas:
Automatic yet explainable age and sex determination
by combining imprecise per-tooth predictions. Com-
puters in Biology and Medicine, 149:1–10.
VISAPP 2024 - 19th International Conference on Computer Vision Theory and Applications
392