TY - GEN
T1 - Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes
AU - Silva, Wilson
AU - Carvalho, Maria
AU - Mavioso, Carlos
AU - Cardoso, Maria J.
AU - Cardoso, Jaime S.
N1 - Funding Information:
This work was partially funded by the Project TAMI-Transparent Artificial Medical Intelligence (NORTE-01-0247-FEDER-045905) financed by ERDF-European Regional Fund through the North Portugal Regional Operational Program-NORTE 2020 and by the Portuguese Foundation for Science and Technology-FCT under the CMU-Portugal International Partnership, and also by the Portuguese Foundation for Science and Technology-FCT within PhD grant number SFRH/BD/139468/2018.
Funding Information:
Acknowledgement. This work was partially funded by the Project TAMI - Transparent Artificial Medical Intelligence (NORTE-01-0247-FEDER-045905) financed by ERDF - European Regional Fund through the North Portugal Regional Operational Program - NORTE 2020 and by the Portuguese Foundation for Science and Technology - FCT under the CMU - Portugal International Partnership, and also by the Portuguese Foundation for Science and Technology - FCT within PhD grant number SFRH/BD/139468/2018.
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022/4/26
Y1 - 2022/4/26
N2 - Treatments for breast cancer have continued to evolve and improve in recent years, resulting in a substantial increase in survival rates, with approximately 80% of patients having a 10-year survival period. Given the serious that impact breast cancer treatments can have on a patient’s body image, consequently affecting her self-confidence and sexual and intimate relationships, it is paramount to ensure that women receive the treatment that optimizes both survival and aesthetic outcomes. Currently, there is no gold standard for evaluating the aesthetic outcome of breast cancer treatment. In addition, there is no standard way to show patients the potential outcome of surgery. The presentation of similar cases from the past would be extremely important to manage women’s expectations of the possible outcome. In this work, we propose a deep neural network to perform the aesthetic evaluation. As a proof-of-concept, we focus on a binary aesthetic evaluation. Besides its use for classification, this deep neural network can also be used to find the most similar past cases by searching for nearest neighbours in the high-semantic space before classification. We performed the experiments on a dataset consisting of 143 photos of women after conservative treatment for breast cancer. The results for accuracy and balanced accuracy showed the superior performance of our proposed model compared to the state of the art in aesthetic evaluation of breast cancer treatments. In addition, the model showed a good ability to retrieve similar previous cases, with the retrieved cases having the same or adjacent class (in the 4-class setting) and having similar types of asymmetry. Finally, a qualitative interpretability assessment was also performed to analyse the robustness and trustworthiness of the model.
AB - Treatments for breast cancer have continued to evolve and improve in recent years, resulting in a substantial increase in survival rates, with approximately 80% of patients having a 10-year survival period. Given the serious that impact breast cancer treatments can have on a patient’s body image, consequently affecting her self-confidence and sexual and intimate relationships, it is paramount to ensure that women receive the treatment that optimizes both survival and aesthetic outcomes. Currently, there is no gold standard for evaluating the aesthetic outcome of breast cancer treatment. In addition, there is no standard way to show patients the potential outcome of surgery. The presentation of similar cases from the past would be extremely important to manage women’s expectations of the possible outcome. In this work, we propose a deep neural network to perform the aesthetic evaluation. As a proof-of-concept, we focus on a binary aesthetic evaluation. Besides its use for classification, this deep neural network can also be used to find the most similar past cases by searching for nearest neighbours in the high-semantic space before classification. We performed the experiments on a dataset consisting of 143 photos of women after conservative treatment for breast cancer. The results for accuracy and balanced accuracy showed the superior performance of our proposed model compared to the state of the art in aesthetic evaluation of breast cancer treatments. In addition, the model showed a good ability to retrieve similar previous cases, with the retrieved cases having the same or adjacent class (in the 4-class setting) and having similar types of asymmetry. Finally, a qualitative interpretability assessment was also performed to analyse the robustness and trustworthiness of the model.
KW - Aesthetic evaluation
KW - Breast cancer
KW - Deep learning
KW - Image retrieval
KW - Interpretability
UR - http://www.scopus.com/inward/record.url?scp=85129879819&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-04881-4_9
DO - 10.1007/978-3-031-04881-4_9
M3 - Conference contribution
AN - SCOPUS:85129879819
SN - 9783031048807
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 108
EP - 118
BT - Pattern Recognition and Image Analysis - 10th Iberian Conference, IbPRIA 2022, Proceedings
A2 - Pinho, Armando J.
A2 - Georgieva, Petia
A2 - Teixeira, Luís F.
A2 - Sánchez, Joan Andreu
PB - Springer Science and Business Media Deutschland GmbH
T2 - 10th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2022
Y2 - 4 May 2022 through 6 May 2022
ER -
