activity
20242026
most citedKey Patches Are All You Need: A Multiple Instance Learning Framework For Robust Medical Diagnosis

1 citations · 2 across the 7 of their papers we have counts for

collaborators

6 papers

eess.IV2026

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…

cs.CV2026

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…

eess.IV2025

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…

cs.CV2025

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…

eess.IV2024

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…

cs.CV20241 cited

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…