11 citations · 12 across the 5 of their papers we have counts for
5 papers
Multimodal Stepwise Clinically-Guided Attention Learning for Pathological Complete Response Prediction in Breast Cancer
Alice Natalina Caragliano, Valerio Guarrasi, Michela Gravina +2
Pathological complete response (pCR) is a key prognostic factor in breast cancer patients undergoing neoadjuvant therapy, strongly associated with long-term survival and treatment…
Learning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLC
Alice Natalina Caragliano, Giulia Farina, Fatih Aksu +10
Major pathological response (pR) following neoadjuvant therapy is a clinically meaningful endpoint in non-small cell lung cancer, strongly associated with improved survival. Howeve…
Longitudinal NSCLC Treatment Progression via Multimodal Generative Models
Massimiliano Mantegna, Elena Mulero Ayllón, Alice Natalina Caragliano +10
Predicting tumor evolution during radiotherapy is a clinically critical challenge, particularly when longitudinal changes are driven by both anatomy and treatment. In this work, we…
Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
Alice Natalina Caragliano, Claudia Tacconi, Carlo Greco +7
This study proposes a novel approach combining Multimodal Deep Learning with intrinsic eXplainable Artificial Intelligence techniques to predict pathological response in non-small…
Doctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
Alice Natalina Caragliano, Filippo Ruffini, Carlo Greco +8
Non-small cell lung cancer (NSCLC) remains a major global health challenge, with high post-surgical recurrence rates underscoring the need for accurate pathological response predic…