5 papers
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…
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…