Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Sobre

Sobre

Sou Professora Associada no Departamento de Engenharia Informática da FEUP, Universidade do Porto, e investigadora senior do INESC TEC. Colaborei e fui responsável por projetos nas áreas do património cultural, bases de dados multimédia, recuperação de informação e otimização combinatória. Supervisionei 9 dissertações de doutoramento e 33 de mestrado. Fui responsável técnica do laboratório SAPO/U.Porto durante 5 anos. A minha atividade de ensino desenvolve-se na Engenharia Informática e na Ciência da Informação. A gestão de dados de investigação está no centro da minha atividade de investigação atual. Sou investigadora principal do projeto TAIL (FCT/POCI), que desenvolve ferramentas e métodos para a publicação de dados de investigação e responsável pelo piloto DataPublication@U.Porto da iniciativa europeia EUDAT. Sou membro do Grupo de Trabalho para a Política Nacional da Ciência Aberta da SECTES. Os meus interesses de investigação incluem a recuperação de informação, a preservação digital e a representação de conhecimento.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Cristina Ribeiro
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 abril 1985
007
Publicações

2022

Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes

Autores
Silva, W; Carvalho, M; Mavioso, C; Cardoso, MJ; Cardoso, JS;

Publicação
PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2022)

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.

2022

Fostering the Adoption of DMP in Small Research Projects through a Collaborative Approach

Autores
Maciel, A; Castro, JA; Ribeiro, C; Almada, M; Midão, L;

Publicação
Int. J. Digit. Curation

Abstract

2021

Report on the 1st linked archives international workshop (LinkedArchives 2021) at TPDL 2021

Autores
Lopes, CT; Ribeiro, C; Niccolucci, F; Rodrigues, IP; Antunes Freire, NM;

Publicação
SIGIR Forum

Abstract

2021

Institutional support for data management plans: case studies for a systematic approach

Autores
Karimova, Y; Ribeiro, C; David, G;

Publicação
Int. J. Metadata Semant. Ontologies

Abstract
Researchers have to ensure that their projects comply with Research Data Management (RDM) requirements. Consequently, the main funding agencies require Data Management Plans (DMPs) for grant applications. So, institutions are investing in RDM tools and implementing RDM workflows in order to support their researchers. In this context, we propose a collaborative DMP-building method that involves researchers, data stewards and other parties if required. This method was applied as part of an RDM workflow in research groups across several scientific domains. We describe it as a systematic approach and illustrate it through a set of case studies. We also address the DMP monitoring process during the life cycle of projects. The feedback from the researchers highlighted the advantages of creating DMPs and their growing need. So, there is motivation to improve the DMP support process according to the machine-actionable DMPs concept and to the best practices in each scientific community. © 2021 Inderscience Enterprises Ltd.. All rights reserved.

2020

ArchOnto, a CIDOC-CRM-Based Linked Data Model for the Portuguese Archives

Autores
Koch, I; Ribeiro, C; Lopes, CT;

Publicação
Digital Libraries for Open Knowledge - 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, Lyon, France, August 25-27, 2020, Proceedings

Abstract

Teses
supervisionadas

2021

Automation of Enterprise Architecture Discovery based on Event Mining from API Gateway logs

Autor
Carlos Pinheiro

Instituição
UTAD

2021

Análise de oferta de serviços na área de consultoria informática

Autor
Pedro Manuel Almeida Roseira

Instituição
UP-FEUP

2021

Analytical Tweezers for cell manipulation and diagnostic

Autor
Inês Alves Carvalho

Instituição
UP-FCUP

2021

Urban transport evaluation using knowledge extracted from social media

Autor
Francisco André Barreiros Murços

Instituição
UP-FEUP

2021

Os consumidores millennials de marcas de luxo: quais as suas especificidades e diferenças?

Autor
Alexandra Oliveira Frade

Instituição
UP-FEP