Seminars 25/26

The TIA seminar is a regular event, organised by the Tissue Image Analytics Centre, that usually takes place on the 1st and 3rd Mondays of each month between 2pm and 3pm. We invite researchers and leaders in the area of computational pathology and associated areas to discuss their work and stimulate thought-provoking discussions.

Take a look at the details of TIA seminars during 2023-24 academic year, most of which are also available on our YouTube channel.

Please contact the TIA Seminar Organiser Dr Adam Shephard via email (adam.shephard@warwick.ac.uk) if you are interested in attending a particular seminar below, or even giving a seminar. Alternatively, please fill-out the following form if you are interested in registering for the seminar series mailing list TIA Seminar Series Registration.

Note. We will continue to update this page with more details about the upcoming seminars.

Autumn 2025

Dr Nadieh Khalili

Radbound UMC, Nijmegen, The Netherlands (click here for a short bio)

13 October 2025

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.

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Dr Tapabrata Chakraborty

University College London, London, UK (click here for a short bio)

27 October 2025

Title: Personalised Predictions with Transparent AI on Multimodal Health Data

Abstract: AI based decision systems have reached near human performance in a range of unimodal tasks, hence the next immediate frontier for AI in the long road towards artificial general intelligence is multimodal AI, that is AI that can handle input and/or output multiple data types seamlessly. There has been significant progress in this area in the past couple of years, but for such systems to be widely used in high-risk applications like healthcare, they need to be transparent and personalised. This talk will present an overview of the recent methods that have been developed in this area from Dr. Chakraborty’s Transparent and Responsible AI Lab (TRAIL).