Inês Domingues

Inês Domingues graduated in Applied Mathematics in the School of Sciences at the University of Porto in 2004, completed the Master in Electrical and Telecommunications Engineering at Aveiro University in 2008 and finished the PhD in Electrical and Computer Engineering in the School of Engineering at the University of Porto in 2015 (Cum Laude). She is currently a post doctoral researcher in the ESTIMA project (IPO Porto + Universidade de Coimbra) and an Invited Assistant Professor at DEIS-ISEC.

Other Websites: LinkedIn profile, Google Scholar Citations; Research Gate profile; Scopus profile; ResearcherId; ORCID; Publons. Identificador na plataforma CIÊNCIA ID 971F-F25B-1E79

For a more detailed CV, see the attached file below.

Highlights:

  • Special Issue Call for Papers - Information Fusion for Medical Data: early, late and deep fusion methods for multimodal data (more details here)
  • I am on the Program Committee of the WorldCIST’19 - 6th World Conference on Information Systems and Technologies. Check more at: http://worldcist.org/index.php/committees
  • Special Issue Call for Papers - Multimedia Systems and Applications in Biomedicine (more details here)

News:

  • [2018, Jul] Gisèle Pereira had her paper "Registration of CT with PET: a comparison of intensity-based approaches" accepted at the International Workshop on Combinatorial Image Analysis (IWCIA)
  • [2018, Jul] Gisèle Pereira finished her MSc entitled "Deep Learning techniques for the evaluation of response to treatment in Hodgkin Lymphoma" at Coimbra University
  • [2018, May] We have our paper "BI-RADS classification of breast cancer: a new pre-processing pipeline for deep models training" accepted at IEEE International Conference on Image Processing (ICIP)
  • [2018, Mar] We have our paper "Evaluation of oversampling data balancing techniques in the context of ordinal classification" accepted at International Joint Conference on Neural Networks (IJCNN)
  • [2018, Jan] José P. Amorim had his paper "Interpreting Deep Learning Models for Ordinal Problems" accepted at European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

Some of the most relevant publications include (bib file with the full list at the bottom):

  • I. Domingues, P. H. Abreu,and J. Santos, J. “BI-RADS classification of breast cancer: a new pre-processing pipeline for deep models training”. In IEEE International Conference on Image Processing (ICIP), 2018.
  • I. C. Moreira, I. Amaral, I. Domingues, A. Cardoso, M. J. Cardoso, and J. S. Cardoso, “INbreast: Toward a Full-field Digital Mammographic Database,” Acad. Radiol., vol. 19, no. 2, pp. 236–248, 2012.
  • P. Martins, I. Carbone, A. Pinto, A. Silva, and A. Teixeira, “European Portuguese MRI based speech production studies,” Speech Commun., vol. 50, no. 11–12, pp. 925–952, 2008.
  • I. Domingues and J. S. Cardoso, “Max Ordinal Learning”, IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 7, 2014.
  • I. Domingues et al., “Pectoral muscle detection in mammograms based on the shortest path with endpoints learnt by SVMs,” in Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 3158–3161, 2010.
  • J. S. Cardoso, R. Sousa, and I. Domingues, “Ordinal Data Classification Using Kernel Discriminant Analysis: A Comparison of Three Approaches,” 11th Int. Conf. Mach. Learn. Appl., pp. 473–477, 2012.