The Tissue Image Analytics (TIA) Centre hosts a seminar series for those interested in computational pathology. The series invites researchers and leaders in computational pathology and related fields to present their work and foster thought-provoking discussions.

The next seminar will take place on Monday, 13 October, from 2–3 p.m. UK time. The Centre is delighted to announce that the session will feature Dr. Nadieh Khalili from Radboud UMC, The Netherlands, who will be joining online.

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 Radboud UMC, 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.