Disentangled and Interpretable Multimodal Attention Fusion for Cancer Survival Prediction

no code implementations • 20 Mar 2025 • Aniek Eijpe, Soufyan Lakbir, Melis Erdal Cesur, Sara P. Oliveira, Sanne Abeln, Wilson Silva

We evaluate DIMAF on four public cancer survival datasets, achieving a relative average improvement of 1. 85% in performance and 23. 7% in disentanglement compared to current state-of-the-art multimodal models.

Disentanglement Survival Prediction +1

An interpretable machine learning system for colorectal cancer diagnosis from pathology slides

no code implementations • 6 Jan 2023 • Pedro C. Neto, Diana Montezuma, Sara P. Oliveira, Domingos Oliveira, João Fraga, Ana Monteiro, João Monteiro, Liliana Ribeiro, Sofia Gonçalves, Stefan Reinhard, Inti Zlobec, Isabel M. Pinto, Jaime S. Cardoso

On the internal dataset, the method shows an accuracy of 93. 44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0. 996.

Active Learning Interpretable Machine Learning +1

CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance

no code implementations • Scientific Reports 2021 • Sara P. Oliveira, Pedro C. Neto, João Fraga, Diana Montezuma, Ana Monteiro, João Monteiro, Liliana Ribeiro, Sofia Gonçalves, Isabel M. Pinto, Jaime S. Cardoso

Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples.

Colorectal Polyps Characterization whole slide images