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BEGIN:VEVENT
DTSTAMP:20260501T140446Z
DTSTART;VALUE=DATE-TIME:20250721T140000
DTEND;VALUE=DATE-TIME:20250721T150000
SUMMARY:TIA Centre Seminar Series: Nadieh Khalili (Radbound UMC)
TZID:Europe/London
UID:20250721-8ac672c7979c9d7b01979e0a0f2a0892@warwick.ac.uk
CREATED:20250716T104429Z
DESCRIPTION:Title: Towards Multimodal AI for Personalized Cancer Treatmen
 t Abstract: The integration of diverse data types—such as histopathology
 \, radiology\, and omics—offers a powerful opportunity to develop AI mod
 els that improve cancer diagnosis and treatment selection. In this talk\
 , I will present recent efforts from our group in developing and validat
 ing multimodal deep learning frameworks that predict treatment-relevant 
 biomarkers directly from routine diagnostic images. I will highlight cas
 e 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 devel
 oping 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 E
 uropean Society of Digital Pathology. How to attend: Either turn up to t
 he event on the day\, or if you want to attend online then please contac
 t Adam Shephard (adam.shephard@warwick.ac.uk) for more details.
LOCATION:MB 2.23
URL:/fac/cross_fac/tia/seminars/seminars-24-25/#nadi
 eh_khalili
ATTACH:/fac/cross_fac/tia/seminars/seminars-24-25/#n
 adieh_khalili
CATEGORIES:Applied Computing
LAST-MODIFIED:20250716T104429Z
ORGANIZER;CN=Adam Shephard:
END:VEVENT
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