Applied Computing Events

Title: Towards Multimodal AI for Personalized Cancer Treatment

Abstract: The integration of diverse data types—such as histopathology, radiology, and omics—offers a powerful opportunity to develop AI models that improve cancer diagnosis and treatment selection. In this talk, I will present recent efforts from our group in developing and validating multimodal deep learning frameworks that predict treatment-relevant biomarkers directly from routine diagnostic images. I will highlight case studies in bladder and prostate cancer and discuss challenges related to data harmonization, model generalization, and clinical translation.

Bio: Dr. Nadieh Khalili is an Assistant Professor in AI at Radboudumc, where she leads research at the intersection of deep learning, digital pathology, and multi-modal data integration. Her work focuses on developing clinically meaningful AI tools for cancer diagnosis and treatment response prediction. She has a background in biomedical engineering and previously worked in R&D roles in industry. Dr. Khalili is also actively involved in organizing scientific events and is a board member of the European Society of Digital Pathology.

How to attend: Either turn up to the event on the day, or if you want to attend online then please contact Adam Shephard (adam.shephard@warwick.ac.uk) for more details.