1 citations · 2 across the 11 of their papers we have counts for
12 papers
Design Choices for Multimodal Transformer Learning with Missing and Longitudinal Healthcare Data
Rita Cordeiro Mendes, Maria Manuel Pereira, Carlos Santiago +1
Multimodal learning offers the potential to improve predictions by integrating complementary information from heterogeneous data sources. However, multimodal datasets are frequentl…
Real-World Multi-Modal and Longitudinal Lung Cancer Dataset
Rita Cordeiro Mendes, Maria Rita Fonseca Verdelho, Carlos Santiago +1
Multi-modal learning has demonstrated strong potential in medical applications by integrating heterogeneous data sources such as medical imaging, clinical records, and genomics to…
Candidate Generation and Definition-Guided Verification for Sentence-Level Depression Symptom Recognition
Weiming Li, Catarina Barata, Miguel Constante +1
Sentence-level recognition of depression symptoms is challenging because similar expressions can differ in symptom relevance, and language-model inference is insufficiently grounde…
Beyond Points: Spherical Distributional Part Prototypes for Interpretable Classification
Duarte Leão, Diogo Pereira Araújo, Catarina Barata +1
Prototype-based neural networks aim to provide intrinsic interpretability by grounding predictions in a small set of part prototypes. However, modern vision backbones typically ope…
Locating Demographic Bias at the Attention-Head Level in CLIP's Vision Encoder
Alaa Yasser, Kittipat Phunjanna, Marcos Escudero Viñolo +2
Standard fairness audits of foundation models quantify that a model is biased, but not where inside the network the bias resides. We propose a mechanistic fairness audit that combi…
MIL vs. Aggregation: Evaluating Patient-Level Survival Prediction Strategies Using Graph-Based Learning
M Rita Verdelho, Alexandre Bernardino, Catarina Barata
Oncologists often rely on a multitude of data, including whole-slide images (WSIs), to guide therapeutic decisions, aiming for the best patient outcome. However, predicting the pro…
The Role of Graph-based MIL and Interventional Training in the Generalization of WSI Classifiers
Rita Pereira, M. Rita Verdelho, Catarina Barata +1
Whole Slide Imaging (WSI), which involves high-resolution digital scans of pathology slides, has become the gold standard for cancer diagnosis, but its gigapixel resolution and the…
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context Adaptation
Rui Daniel, M. Rita Verdelho, Catarina Barata +1
Deep Learning for medical imaging faces challenges in adapting and generalizing to new contexts. Additionally, it often lacks sufficient labeled data for specific tasks requiring s…
Pinpoint Counterfactuals: Reducing social bias in foundation models via localized counterfactual generation
Kirill Sirotkin, Marcos Escudero-Viñolo, Pablo Carballeira +3
Foundation models trained on web-scraped datasets propagate societal biases to downstream tasks. While counterfactual generation enables bias analysis, existing methods introduce a…
MMIST-ccRCC: A Real World Medical Dataset for the Development of Multi-Modal Systems
Tiago Mota, M. Rita Verdelho, Alceu Bissoto +2
The acquisition of different data modalities can enhance our knowledge and understanding of various diseases, paving the way for a more personalized healthcare. Thus, medicine is p…
Key Patches Are All You Need: A Multiple Instance Learning Framework For Robust Medical Diagnosis
Diogo J. Araújo, M. Rita Verdelho, Alceu Bissoto +3
Deep learning models have revolutionized the field of medical image analysis, due to their outstanding performances. However, they are sensitive to spurious correlations, often tak…
XAI for Skin Cancer Detection with Prototypes and Non-Expert Supervision
Miguel Correia, Alceu Bissoto, Carlos Santiago +1
Skin cancer detection through dermoscopy image analysis is a critical task. However, existing models used for this purpose often lack interpretability and reliability, raising the…