7 citations · 19 across the 5 of their papers we have counts for
5 papers
ViConEx-Med: Visual Concept Explainability via Multi-Concept Token Transformer for Medical Image Analysis
Cristiano Patrício, Luís F. Teixeira, João C. Neves
Concept-based models aim to explain model decisions with human-understandable concepts. However, most existing approaches treat concepts as numerical attributes, without providing…
A Two-Step Concept-Based Approach for Enhanced Interpretability and Trust in Skin Lesion Diagnosis
Cristiano Patrício, Luís F. Teixeira, João C. Neves
The main challenges hindering the adoption of deep learning-based systems in clinical settings are the scarcity of annotated data and the lack of interpretability and trust in thes…
Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion models
Cristiano Patrício, Carlo Alberto Barbano, Attilio Fiandrotti +4
Contrastive Analysis (CA) detects anomalies by contrasting patterns unique to a target group (e.g., unhealthy subjects) from those in a background group (e.g., healthy subjects). I…
Towards Concept-based Interpretability of Skin Lesion Diagnosis using Vision-Language Models
Cristiano Patrício, Luís F. Teixeira, João C. Neves
Concept-based models naturally lend themselves to the development of inherently interpretable skin lesion diagnosis, as medical experts make decisions based on a set of visual patt…
Explainable Deep Learning Methods in Medical Image Classification: A Survey
Cristiano Patrício, João C. Neves, Luís F. Teixeira
The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though state-of-the-art deep learning models have achieved human…