Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes

Wilson Silva, Maria Carvalho, Carlos Mavioso, Maria J. Cardoso, Jaime S. Cardoso

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationPattern Recognition and Image Analysis - 10th Iberian Conference, IbPRIA 2022, Proceedings
EditorsArmando J. Pinho, Petia Georgieva, Luís F. Teixeira, Joan Andreu Sánchez
PublisherSpringer Science and Business Media Deutschland GmbH
Pages108-118
Number of pages11
ISBN (Print)9783031048807
DOIs
Publication statusPublished - 26 Apr 2022
Event10th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2022 - Aveiro, Portugal
Duration: 4 May 20226 May 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13256 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2022
Country/TerritoryPortugal
CityAveiro
Period4/05/226/05/22

Keywords

  • Aesthetic evaluation
  • Breast cancer
  • Deep learning
  • Image retrieval
  • Interpretability

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