Zunamys Itzell Carrero

MoPaDi generates counterfactual explanations for histopathology images to reveal morphological features driving classifier predictions, improving interpretability in digital pathology.

This study develops an ex vivo drug sensitivity testing platform for rare solid tumors and finds that high in vitro sensitivity correlates with clinical benefit and progression-free survival.

This study integrates multi-omic and multimodal data from IBD patients to identify molecular, histopathological, and clinical features associated with inflammatory bowel diseases.

This study compares deep learning and machine learning for biomarker prediction in cancer genomes, finding DL performs better in cross-assay and data-sparse settings.

This study uses attention-based multiple instance learning to predict HRD status from H&E images across ten tumor types, achieving moderate to high AUROC values.