Personal identification

Full name

José Pedro Pereira Amorim

Citation names

• Amorim, José Pereira

Author identifiers

Ciência ID

0813-E00E-1F0C

ORCID iD

0000-0002-9477-0078

Languages

Language	Speaking	Reading	Writing	Listening	Peer-review
Portuguese (Mother tongue)
English	Advanced (C1)	Advanced (C1)	Advanced (C1)	Advanced (C1)	Advanced (C1)

	Degree	Classification
2017/09/01 - 2022/12/01 Ongoing	Doutoramento em Engenharia Informática (Doutoramento) Universidade de Coimbra Faculdade de Ciencias e Tecnologia, Portugal
2012/09/01 - 2017/01/07 Concluded	Engenharia Informática e Computação (Mestrado integrado) Universidade do Porto Faculdade de Engenharia, Portugal

Teaching in Higher Education

	Category Host institution	Employer
2020/09/01 - Current	Invited Assistant (University Teacher)	Universidade de Coimbra, Portugal
	Universidade de Coimbra Faculdade de Ciencias e Tecnologia, Portugal

Grant

	Designation	Funders
2018/09/01 - 2022/12/01	Research Grant SFRH/BD/136786/2018 SFRH/BD/136786/2018 PhD Student Fellow	Fundação para a Ciência e a Tecnologia
2017/09/01 - 2018/09/01	Early-stage cancer treatment, driven by context of molecular imaging (ESTIMA) NORTE-01-0145-FEDER-000027 PhD Student Fellow Instituto Português de Oncologia do Porto Francisco Gentil Centro de Investigação, Portugal	Fundação para a Ciência e a Tecnologia
2015/04/01 - 2015/07/01	Projeto SIBILA NORTE-07-0124-FEDER-000059 Scientific Initiation Fellow	Fundação para a Ciência e a Tecnologia

Publications

Book chapter	Cardoso, Jaime; Van Nguyen, Hien; Heller, Nicholas; Henriques Abreu, Pedro; Isgum, Ivana; Silva, Wilson; Cruz, Ricardo; et al. "Interpretable and Annotation-Efficient Learning for Medical Image Computing". In Interpretable and Annotation-Efficient Learning for Medical Image Computing, C1-C1. Springer International Publishing, 2020. 10.1007/978-3-030-61166-8_30 • Editor
Conference paper	Amorim, José Pereira; Abreu, Pedro H.; Reyes, Mauricio; Santos, Joao. Corresponding author: Amorim, José Pereira. "Interpretability vs. Complexity: The Friction in Deep Neural Networks". 2020. 10.1109/ijcnn48605.2020.9206800 Ricardo Cardoso Pereira; Joana Cristo Santos; Amorim, José; Pedro Pereira Rodrigues; Henriques Abreu, Pedro. Corresponding author: Ricardo Cardoso Pereira. "Missing Image Data Imputation using Variational Autoencoders with Weighted Loss". Paper presented in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2020. Marques, Francisco; Duarte, Hugo; Santos, João; Domingues, Inês; Amorim, José P.; Abreu, Pedro H.. "An iterative oversampling approach for ordinal classification". 2019. 10.1145/3297280.3297560 Domingues, Ines; Amorim, Jose P.; Abreu, Pedro H.; Duarte, Hugo; Santos, Joao. "Evaluation of Oversampling Data Balancing Techniques in the Context of Ordinal Classification". 2018. 10.1109/ijcnn.2018.8489599 Amorim, José Pereira; Domingues, Inês; Henriques Abreu, Pedro. "Interpreting Deep Learning Models for Ordinal Problems". Paper presented in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018. Published
Journal article	Amorim, José Pereira; Henriques Abreu, Pedro; Santos, João A. M; Müller, Henning. Corresponding author: Amorim, José Pereira. "Evaluating Post-hoc Interpretability with Intrinsic Interpretability". Decision Support Systems (2023): https://arxiv.org/abs/2305.03002. Submitted Amorim, José Pereira; Abreu, Pedro H.; Santos, João; Cortes, Marc; Vila, Victor. Corresponding author: Amorim, José Pereira. "Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations". Information Processing & Management 60 2 (2023): 103225. http://dx.doi.org/10.1016/j.ipm.2022.103225. 10.1016/j.ipm.2022.103225 Graziani, Mara; Dutkiewicz, Lidia; Calvaresi, Davide; Amorim, José Pereira; Yordanova, Katerina; Vered, Mor; Nair, Rahul; et al. "A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences". Artificial Intelligence Review (2022): http://dx.doi.org/10.1007/s10462-022-10256-8. 10.1007/s10462-022-10256-8 Amorim, José Pereira; Henriques Abreu, Pedro; Fernandéz, Alberto; Reyes, Mauricio; Santos, João A. M.; Henriques Abreu, Miguel. Corresponding author: Amorim, José Pereira. "Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists". IEEE Reviews in Biomedical Engineering 16 1 (2021): 192-207. http://dx.doi.org/10.1109/rbme.2021.3131358. Published • 10.1109/rbme.2021.3131358