Publications

Publications by Jaime Cardoso

2020

Interpretable and Annotation-Efficient Learning for Medical Image Computing - Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings

Authors
Cardoso, JS; Nguyen, HV; Heller, N; Abreu, PH; Isgum, I; Silva, W; Cruz, R; Amorim, JP; Patel, V; Roysam, B; Zhou, SK; Jiang, SB; Le, N; Luu, K; Sznitman, R; Cheplygina, V; Mateus, D; Trucco, E; Sureshjani, SA;

Publication
iMIMIC/MIL3ID/LABELS@MICCAI

Abstract

2022

Interpretability of Machine Intelligence in Medical Image Computing - 5th International Workshop, iMIMIC 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings

Authors
Reyes, M; Abreu, PH; Cardoso, JS;

Publication
iMIMIC@MICCAI

Abstract

2021

Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data - 4th International Workshop, iMIMIC 2021, and 1st International Workshop, TDA4MedicalData 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

Authors
Reyes, M; Abreu, PH; Cardoso, JS; Hajij, M; Zamzmi, G; Paul, R; Thakur, L;

Publication
iMIMIC/TDA4MedicalData@MICCAI

Abstract

2019

Sparse Multi-Bending Snakes

Authors
Araújo, RJ; Fernandes, K; Cardoso, JS;

Publication
IEEE Trans. Image Process.

Abstract

2015

The vitality of pattern recognition and image analysis

Authors
Micó, L; Sanches, JM; Cardoso, JS;

Publication
Neurocomputing

Abstract

2023

Disentangled Representation Learning for Privacy-Preserving Case-Based Explanations

Authors
Montenegro, H; Silva, W; Cardoso, JS;

Publication
MEDICAL APPLICATIONS WITH DISENTANGLEMENTS, MAD 2022

Abstract
The lack of interpretability of Deep Learning models hinders their deployment in clinical contexts. Case-based explanations can be used to justify these models' decisions and improve their trustworthiness. However, providing medical cases as explanations may threaten the privacy of patients. We propose a generative adversarial network to disentangle identity and medical features from images. Using this network, we can alter the identity of an image to anonymize it while preserving relevant explanatory features. As a proof of concept, we apply the proposed model to biometric and medical datasets, demonstrating its capacity to anonymize medical images while preserving explanatory evidence and a reasonable level of intelligibility. Finally, we demonstrate that the model is inherently capable of generating counterfactual explanations.

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