João A. M. Santos

Orcid: 0000-0003-2465-5143

Affiliations:
  • University of Porto, Portuguese Institute of Oncology of Porto / Instituto de Ciências Biomédicas Abel Salazar, Medical Physics Department, Portugal


According to our database1, João A. M. Santos authored at least 27 papers between 2012 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Online presence:

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Bibliography

2023
Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations.
Inf. Process. Manag., 2023

A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research.
Inf. Fusion, 2023

Evaluating Post-hoc Interpretability with Intrinsic Interpretability.
CoRR, 2023

2022
The impact of heterogeneous distance functions on missing data imputation and classification performance.
Eng. Appl. Artif. Intell., 2022

Many-objective optimization of a three-echelon supply chain: A case study in the pharmaceutical industry.
Comput. Ind. Eng., 2022

On the joint-effect of class imbalance and overlap: a critical review.
Artif. Intell. Rev., 2022

2020
How distance metrics influence missing data imputation with k-nearest neighbours.
Pattern Recognit. Lett., 2020

Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET.
Artif. Intell. Rev., 2020

Interpretability vs. Complexity: The Friction in Deep Neural Networks.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Classification of oesophagic early-stage cancers: deep learning versus traditional learning approaches.
Proceedings of the 20th IEEE International Conference on Bioinformatics and Bioengineering, 2020

Assessing the Impact of Distance Functions on K-Nearest Neighbours Imputation of Biomedical Datasets.
Proceedings of the Artificial Intelligence in Medicine, 2020

2019
Generating Synthetic Missing Data: A Review by Missing Mechanism.
IEEE Access, 2019

Computer Vision in Esophageal Cancer: A Literature Review.
IEEE Access, 2019

An iterative oversampling approach for ordinal classification.
Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 2019

Going Back to Basics on Volumetric Segmentation of the Lungs in CT: A Fully Image Processing Based Technique.
Proceedings of the Pattern Recognition and Image Analysis - 9th Iberian Conference, 2019

Automatic Generation of Lymphoma Post-Treatment PETs using Conditional-GANs.
Proceedings of the 2019 Digital Image Computing: Techniques and Applications, 2019

2018
Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier].
IEEE Comput. Intell. Mag., 2018

Registration of CT with PET: A Comparison of Intensity-Based Approaches.
Proceedings of the Combinatorial Image Analysis - 19th International Workshop, 2018

Evaluation of Oversampling Data Balancing Techniques in the Context of Ordinal Classification.
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

Exploring the Effects of Data Distribution in Missing Data Imputation.
Proceedings of the Advances in Intelligent Data Analysis XVII, 2018

Analysing the Footprint of Classifiers in Overlapped and Imbalanced Contexts.
Proceedings of the Advances in Intelligent Data Analysis XVII, 2018

Bi-Rads Classification of Breast Cancer: A New Pre-Processing Pipeline for Deep Models Training.
Proceedings of the 2018 IEEE International Conference on Image Processing, 2018

Interpreting deep learning models for ordinal problems.
Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

2017
An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images.
BMC Medical Imaging, 2017

Image descriptors in radiology images: a systematic review.
Artif. Intell. Rev., 2017

Influence of Data Distribution in Missing Data Imputation.
Proceedings of the Artificial Intelligence in Medicine, 2017

2012
Lightweight Automatic Error Detection by Monitoring Collar Variables.
Proceedings of the Testing Software and Systems, 2012


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