Research 🔬

Research topics

My current research interests span the fields of Machine Learning/Artificial Intelligence, Computer Vision, and Health Informatics, in particular, their intersection. Nonetheless, I also have an interest in Data Science, Natural Computing, and Decision Making. A list of topics is presented in the following:

• Machine learning
• Deep Learning
• Medical imaging
• Medical Datasets
• ML for Health and wellbeing
• Data Science
• Decision making
• Information fusion
• Evolutionary algorithms
• Robotics

Research groups

I’ve worked in the following research groups:

• 2023 - now: Artificial Intelligence in Health Laboratory (LIFE), Federal University of Espírito Santo Vitória, Brasil
• 2022 - now: Institute of Applied Computacional Intelligence – I²CA, Federal University of Espírito Santo Vitória, Brasil
• 2020 - 2022: AI R&D Lab - Samsung Research Institute Brazil (SRBR) Campinas, Brazil
• 2019 - 2020: Hierarchical Anticipatory Learning Lab. (HALLab), Dalhousie University Halifax, Canada
• 2016 - 2020: Bio-inspired Laboratory (LABCIN), Federal University of Espírito Santo Vitória, Brasil
• 2013 - 2016: Optimization laboratory (LabOtim), Federal University of Espírito Santo Vitória, Brasil

Conference and Journal Reviewing

• 2024: Computers in Biology and Medicine, Artificial Intelligence in Medicine
• 2023: Artificial Intelligence in Medicine, Biomedical Signal Processing and Control, IEEE Journal of Biomedical and Health Informatics, and MethodsX
• 2022: IEEE Journal of Biomedical and Health Informatics
• 2021: IEEE Journal of Biomedical and Health Informatics, Medical Image Analysis, ISIC @ CVPR (program committee)
• 2020: IEEE Journal of Biomedical and Health Informatics
• 2019: Computers & Industrial Engineering, IEEE International Joint Conference on Neural Networks, and Neurocomputing
• 2018: IEEE International Joint Conference on Neural Networks

Thesis

• PhD thesis: Combining heterogeneous data and deep learning models for skin cancer detection
• Master thesis (pt-br only): Agregação de classificadores neurais via integral de Choquet com respeito a uma medida fuzzy

List of publications

This is my list of publications. It’s organized in chronological time. You can also check my Google Scholar.

• LiwTERM: A Lightweight Transformer-based Model for Dermatological Multimodal Lesion Detection
• Authors: Luis A. de Souza Jr., André GC Pacheco, Gabriel G. de Angelo, Thiago Oliveira-Santos, Christoph Palm & João P. Papa
• In: SIBGRAPI 2024

• DeepCraftFuse: visual and deeply-learnable features work better together for esophageal cancer detection in patients with Barrett’s esophagus
• Authors: Luis A Souza Jr, André GC Pacheco, Leandro A Passos, Marcos CS Santana, Robert Mendel, Alanna Ebigbo, Andreas Probst, Helmut Messmann, Christoph Palm, João Paulo Papa
• In: Neural Computing and Applications

• DeepCraftFuse: visual and deeply-learnable features work better together for esophageal cancer detection in patients with Barrett’s esophagus
• Authors: Luis A Souza Jr, André GC Pacheco, Leandro A Passos, Marcos CS Santana, Robert Mendel, Alanna Ebigbo, Andreas Probst, Helmut Messmann, Christoph Palm, João Paulo Papa
• In: Neural Computing and Applications

• SADE: Software de Análise Dermatológica - Um sistema de coleta, gerenciamento e triagem de lesões de pele
• Authors: Andre G. C. Pacheco, Clayton Oliveira Vicente, Eduarda Pylro Magesk, Gabriel Schettino Lucas, Guilherme Teixeira Caldana, Patricia H. L. Frasson
• In: Anais estendidos do Simpósio Brasileiro de Sistemas Multimídia e Web (webmedia)

• Towards low-power heart rate estimation based on user’s demographics and activity level for wearable
• Authors: Andre G. C. Pacheco, Frank A. C. Cabello, Paula G. Rodrigues, Desiree C. Miraldo, Paula R. Pinto, Adriana M. O. Fonoff, and Otavio A. B. Penatti
• In: IEEE International Conference on Acoustics, Speech and Signal Processing

• Learning to estimate heart rate from accelerometer and user’s demographics during physical exercises
• Authors: Andre G. C. Pacheco, Frank A. C. Cabello, Paula G. Rodrigues, Desiree C. Miraldo, Vanessa B. O. Fioravanti, Rafael G. Lima, Paula R. Pinto, Adriana M. O. Fonoff, and Otavio A. B. Penatti
• In: IEEE Journal of Biomedical and Health Informatics

• Learning multiplane images from single views with self-supervision
• Authors: Gustavo S. P. Carvalho, Diogo C. Luvizon, Antonio Joia, Andre G. C. Pacheco, and Otavio A. B. Penatti
• In: British Machine Vision Conference (BMVC) 2021
• Page: https://cyclempi.github.io/
• Presentation: Link

• Improving Deep Learning Sound Events Classifiers Using Gram Matrix Feature-Wise Correlations
• Authors: Antonio Joia, Andre G. C. Pacheco, and Diogo Luvizon
• In: IEEE International Conference on Acoustics, Speech and Signal Processing
• Code: https://github.com/a-joia/Gram-Classifier

• An attention-based mechanism to combine images and metadata in deep learning models applied to skin cancer classification
• Authors: Andre G. C. Pacheco and Renato Krohling
• In: IEEE Journal of Biomedical and Health Informatics
• Code: https://github.com/paaatcha/MetaBlock

• PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones
• Authors: Andre G. C. Pacheco et al.
• In: Data in Brief
• Data: Link

• The impact of patient clinical information on automated skin cancer detection
• Authors: Andre G. C. Pacheco and Renato Krohling
• In: Computers in Biology and Medicine
• Data: Link

• On Out-of-Distribution Detection Algorithms With Deep Neural Skin Cancer Classifiers
• Authors: Andre G. C. Pacheco, Chandramouli Sastry, Thomas Trappenberg, Sageev Oore, and Renato Krohling
• In: IEEE computer society conference on Computer Vision and Pattern Recognition 2020 @ ISIC 2020 workshop
• Code: https://github.com/paaatcha/gram-ood
• Presentation: Link
• Award: Best paper award 🥇

• Learning dynamic weights for an ensemble of deep models applied to medical imaging classification
• Authors: Andre G. C. Pacheco, Thomas Trappenberg, and Renato Krohling
• In: IEEE International Joint Conference in Neural Networks, 2020

• An App to Detect Melanoma Using Deep Learning: An Approach to Handle Imbalanced Data Based on Evolutionary Algorithms
• Authors: Pedro Castro, Breno Krohling, Andre G. C. Pacheco, and Renato Krohling
• In: IEEE International Joint Conference in Neural Networks, 2020

• Skin lesion segmentation using deep learning for images acquired from smartphones
• Authors: Gabriel de Angelo, Andre G. C. Pacheco, and Renato Krohling
• In: IEEE International Joint Conference in Neural Networks 2019

• Skin cancer detection based on deep learning and entropy to detect outlier samples
• Authors: Andre Pacheco, Alib Abder, and Thomas Trappenberg
• In: MICCAI 2019 @ ISIC Challenge, 2019
• Code: https://github.com/paaatcha/jedy

• Recent advances in deep learning applied to skin cancer detection
• Authors: Andre G. C. Pacheco and Renato Krohling
• In: NeurIPS 2019 @ Retrospectives workshop
• Presentation: Link

• Aggregation of neural classifiers using Choquet integral with respect to a fuzzy measure
• Authors: Andre G. C. Pacheco and Renato Krohling
• In: Neurocomputing, 2020

• Ranking of Classification Algorithms in Terms of Mean-Standard Deviation Using A-TOPSIS
• Authors: Andre G. C. Pacheco and Renato Krohling
• In: Annals of Data Science, 2016

• An approach to improve online sequential extreme learning machines using a restricted Boltzmann machine
• Authors: Andre G. C. Pacheco and Renato Krohling
• In: IEEE International Joint Conference in Neural Networks, 2018
• Code: https://github.com/paaatcha/RBM-ELM

• Restricted Boltzmann machine to determine the input weights for extreme learning machines
• Authors: Andre G. C. Pacheco, Carlos Silva, and Renato Krohling
• In: Expert Systems with Applications, 2018
• Code: https://github.com/paaatcha/RBM-ELM

• TODIM and TOPSIS with Z-numbers
• Authors: Renato Krohling, Andre G. C. Pacheco, and Guilherme Artém
• In: Frontiers of Information Technology & Electronic Engineering, 2017

• Agregação de elenco de classificadores utilizando integral de Choquet com respeito a medida λ-fuzzy
• Authors: Renato Krohling and Andre G. C. Pacheco
• In: Simpósio Brasileiro de Pesquisa Operacional, 2016

• Classificação de grandes bases de dados utilizando máquina de Boltzmann restrita discriminativa
• Authors: Renato Krohling, Andre Pacheco, and Guilherme Artém
• In: Simpósio Brasileiro de Pesquisa Operacional, 2016

• A-TOPSIS - An approach Based on TOPSIS for Ranking Evolutionary Algorithms
• Authors: Renato Krohling and Andre Pacheco
• In: Procedia Computer Science, 2015
• Code: https://github.com/paaatcha/decision-making

• Time Series Prediction using Restricted Boltzmann Machines and backpropagation
• Authors: Rafael Hrasko, Andre Pacheco, and Renato Krohling
• In: Procedia Computer Science, 2015

• Interval-valued Intuitionistic Fuzzy TODIM
• Authors: Renato Krohling and Andre Pacheco
• In: Procedia Computer Science, 2014

• IF-TODIM: An intuitionistic fuzzy TODIM to multi-criteria decision making
• Authors: Renato Krohling, Andre Pacheco, and Andre Siviero
• In: Knowledge-Based Systems, 2013

• IFG-TODIM: An intuitionistic fuzzy TODIM for group decision making
• Authors: Renato Krohling and Andre Pacheco
• In: Simpósio Brasileiro de Pesquisa Operacional, 2013

Talks and presentations

Here you may find some talks and presentations I carried out throughout the years. Some are in English and others in Portuguese:

• Sep 2023: PAD-UFES-20: the challenges and opportunities in creating a skin lesion dataset. Presentation
• Ago 2023: Out-of-distribution (OOD) detection: uma visão geral sobre o tópico para classificação de imagens . Download
• Nov 2022: Como a Inteligência Artificial afeta seu cotidiano. Download
• Oct 2022: Ética em Inteligência Artificial - Por que precisamos falar sobre esse assunto?. Download
• Apr 2021: Sobrevivendo na graduação: guia (quase) definitivo. Download
• Jul 2020: On Out-of-Distribution Detection Algorithms With Deep Neural Skin Cancer Classifiers. CVPR 2020 @ ISIC 2020. Download
• Dec 2019: Recent advances in deep learning applied to skin cancer detection. NeurIPS 2019 @ Retrospectives workshop. Download
• Mar 2019: Introduction to machine learning. Faculdade Multivix, Vitória, Brazil. Portuguese only. Download
• Nov 2018: Introduction to Capsule Networks. Computer Science, UFES, Vitória, Brazil. Download
• Nov 2018: Introduction to Helmholtz machine. Computer Science, UFES, Vitória, Brazil. Download
• Jun 2018: An approach to improve online sequential extreme learning machines using restricted Boltzmann machines. IJCNN 2018, Rio de Janeiro, Brazil. Download
• Apr 2018: Restricted Boltzmann Machine for clustering data. Computer Science, UFES, Vitória, Brazil. Download
• Mar 2018: Introduction to Python for scientific computer. Production Engineer, UFES, Vitória, Brazil. Portuguese only. Download
• Sep 2017: Overview of singular value decomposition. Computer Science, UFES. Vitória, UFES. Portuguese only. Download
• Apr 2017: Classification fusion. Computer Science, UFES, Vitória, Brazil. Portuguese only. Download
• Jul 2016: Restricted Boltzmann Machine for classification. SBPO 2016. Vitória, Brazil. Portuguese only. Download
• Jul 2016: Introduction to data classification. Jornada de Atualização em Computação, Elétrica e Eletrônica - JAACE, UFES, Vitória, Brazil. Portuguese only. Download
• Aug 2014: Raking algorithms with A-TOPSIS. ITQM 2014, Rio de Janeiro, Brazil. Download

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