Pedro Henriques Abreu

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Portugal

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Pedro Henriques Abreu is an Assistant Professor at the Faculty of Sciences and Technology, University of Coimbra, in Coimbra, Portugal. His research areas include multi-label image classification, data imputation methodologies, and the application of deep learning in medical imaging. He has contributed to various articles focusing on topics such as adversarial attacks, fairness in machine learning, and the generation of missing data.

Recent Grants

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Smart5Grid - Automated 5G Networks and Services for Smart Grids

Open Date: 2021-01-01

Close Date: 2023-12-01

Grant: Close

Atena - Advanced Tools to assEss and mitigate the criticality of ict compoNents and their dependencies over critical infrAstructures

Open Date: 2016-05-01

Close Date: 2019-05-01

Grant: Close

Estima - Early-stage cancer treatment, driven by context of molecular imaging

Open Date: 2016-03-01

Close Date: 2020-03-01

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SBIRC - Based Image Retrieval and Classi

Open Date: 2011-04-01

Close Date: 2014-03-01

Grant: Close

ACORD - Adaptative Coordination of Robotic Teams

Open Date: 2008-01-01

Close Date:

Pedro Henriques Abreu

Research Interests

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About

Recent Grants

Articles (24)

LogicMix: Sample mixing data augmentation for multi-label image classification with partial labels

Guidelines for designing visualization tools for group fairness analysis in binary classification

Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning

QIDLearningLib: A Python library for quasi-identifier recognition and evaluation

Studying the robustness of data imputation methodologies against adversarial attacks

Category-wise Fine-Tuning: Resisting incorrect pseudo-labels in multi-label image classification with partial labels

The Role of Deep Learning in Medical Image Inpainting: A Systematic Review

Reparameterization convolutional neural networks for handling imbalanced datasets in solar panel fault classification

mdatagen: A python library for the artificial generation of missing data

A Label Propagation Approach for Missing Data Imputation

Imputation of data Missing Not at Random: Artificial generation and benchmark analysis

Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations

A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research

Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists

Bone Metastases Detection in Patients with Breast Cancer: Does Bone Scintigraphy Add Information to PET/CT?

Partial Multiple Imputation With Variational Autoencoders: Tackling Not at Randomness in Healthcare Data

A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences

The impact of heterogeneous distance functions on missing data imputation and classification performance

On the joint-effect of class imbalance and overlap: a critical review

FAWOS: Fairness-Aware Oversampling Algorithm Based on Distributions of Sensitive Attributes

Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET

Reviewing Autoencoders for Missing Data Imputation: Technical Trends, Applications and Outcomes

How distance metrics influence missing data imputation with k-nearest neighbours

Guest Editorial: Information Fusion for Medical Data: Early, Late, and Deep Fusion Methods for Multimodal Data

Collaborators (16)

João Paulo Silva Machado Garcia Vilela

Mario Figueiredo

Catarina Maçãs

Henning Müller

Helder Araujo

Joao Gama

Nathalie Japkowicz

Alberto Fernandez Hilario

Pedro Pereira Rodrigues

Mor Vered

Fernando Jorge Penousal Martins Machado

Inês Domingues

Mauricio Reyes

Adrien Depeursinge

Ricardo Daniel Cardoso Pereira

José Amorim

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