2024
Autores
Gonçalves, T; Hedström, A; Pahud de Mortanges, A; Li, X; Müller, H; Cardoso, S; Reyes, M;
Publicação
Trustworthy Ai in Medical Imaging
Abstract
In the healthcare context, artificial intelligence (AI) has the potential to power decision support systems and help health professionals in their clinical decisions. However, given its complexity, AI is usually seen as a black box that receives data and outputs a prediction. This behavior may jeopardize the adoption of this technology by the healthcare community, which values the existence of explanations to justify a clinical decision. Besides, the developers must have a strategy to assess and audit these systems to ensure their reproducibility and quality in production. The field of interpretable artificial intelligence emerged to study how these algorithms work and clarify their behavior. This chapter reviews several interpretability of AI algorithms for medical imaging, discussing their functioning, limitations, benefits, applications, and evaluation strategies. The chapter concludes with considerations that might contribute to bringing these methods closer to the daily routine of healthcare professionals. © 2025 Elsevier Inc. All rights reserved.
2024
Autores
Caldeira, E; Neto, PC; Gonçalves, T; Damer, N; Sequeira, AF; Cardoso, JS;
Publicação
Science Talks
Abstract
2024
Autores
Beirão, MM; Matos, J; Gonçalves, T; Kase, C; Nakayama, LF; Freitas, Dd; Cardoso, JS;
Publicação
IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024, Lisbon, Portugal, December 3-6, 2024
Abstract
Keratitis is an inflammatory corneal condition responsible for 10% of visual impairment in low- and middle-income countries (LMICs), with bacteria, fungi, or amoeba as the most common infection etiologies. While an accurate and timely diagnosis is crucial for the selected treatment and the patients' sight outcomes, due to the high cost and limited availability of laboratory diagnostics in LMICs, diagnosis is often made by clinical observation alone, despite its lower accuracy. In this study, we investigate and compare different deep learning approaches to diagnose the source of infection: 1) three separate binary models for infection type predictions; 2) a multitask model with a shared backbone and three parallel classification layers (Multitask V1); and, 3) a multitask model with a shared backbone and a multi-head classification layer (Multitask V2). We used a private Brazilian cornea dataset to conduct the empirical evaluation. We achieved the best results with Multitask V2, with an area under the receiver operating characteristic curve (AUROC) confidence intervals of 0.7413-0.7740 (bacteria), 0.83950.8725 (fungi), and 0.9448-0.9616 (amoeba). A statistical analysis of the impact of patient features on models' performance revealed that sex significantly affects amoeba infection prediction, and age seems to affect fungi and bacteria predictions. © 2024 IEEE.
2024
Autores
Beirão, MM; Matos, J; Gonçalves, T; Kase, C; Nakayama, LF; Freitas, Dd; Cardoso, JS;
Publicação
CoRR
Abstract
2024
Autores
Eduard-Alexandru Bonci; Orit Kaidar-Person; Marília Antunes; Oriana Ciani; Helena Cruz; Rosa Di Micco; Oreste Davide Gentilini; Nicole Rotmensz; Pedro Gouveia; Jörg Heil; Pawel Kabata; Nuno Freitas; Tiago Gonçalves; Miguel Romariz; Helena Montenegro; Hélder P. Oliveira; Jaime S. Cardoso; Henrique Martins; Daniela Lopes; Marta Martinho; Ludovica Borsoi; Elisabetta Listorti; Carlos Mavioso; Martin Mika; André Pfob; Timo Schinköthe; Giovani Silva; Maria-Joao Cardoso;
Publicação
Cancer Research
Abstract
2024
Autores
Zolfagharnasab, MH; Freitas, N; Gonçalves, T; Bonci, E; Mavioso, C; Cardoso, MJ; Oliveira, HP; Cardoso, JS;
Publicação
Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care - First Deep Breast Workshop, Deep-Breath 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings
Abstract
Breast cancer treatments often affect patients’ body image, making aesthetic outcome predictions vital. This study introduces a Deep Learning (DL) multimodal retrieval pipeline using a dataset of 2,193 instances combining clinical attributes and RGB images of patients’ upper torsos. We evaluate four retrieval techniques: Weighted Euclidean Distance (WED) with various configurations and shallow Artificial Neural Network (ANN) for tabular data, pre-trained and fine-tuned Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), and a multimodal approach combining both data types. The dataset, categorised into Excellent/Good and Fair/Poor outcomes, is organised into over 20K triplets for training and testing. Results show fine-tuned multimodal ViTs notably enhance performance, achieving up to 73.85% accuracy and 80.62% Adjusted Discounted Cumulative Gain (ADCG). This framework not only aids in managing patient expectations by retrieving the most relevant post-surgical images but also promises broad applications in medical image analysis and retrieval. The main contributions of this paper are the development of a multimodal retrieval system for breast cancer patients based on post-surgery aesthetic outcome and the evaluation of different models on a new dataset annotated by clinicians for image retrieval. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
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