Abstract

Join us for the Cambridge AI in Medicine Seminar Series, hosted by the Cancer Research UK Cambridge Centre and the Department of Radiology at Addenbrooke’s. This series brings together leading experts to explore cutting-edge AI applications in healthcare – from disease diagnosis to drug discovery. It’s a unique opportunity for researchers, practitioners, and students to stay at the forefront of AI innovations and engage in discussions shaping the future of AI in healthcare.

This month’s seminar will be held on Tuesday 27 January 2025, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom. A light lunch from Aromi will be served from 11:45. The event will feature the following talk:

Explainability and forecasting in medical imaging – Ida Häggström, Associate Professor, Unit of Computer Vision and Medical Image Analysis, Chalmers University of Technology, Sweden

Ida is an Associate Professor in the Computer Vision and Medical Image Analysis group at Chalmers University of Technology, in Gothenburg, Sweden, working with machine and deep learning techniques for medical image analysis. She collaborates closely with clinical researchers on projects to diagnose, predict and prognosticate different diseases, mainly cancer. She completed two Master’s degrees in Engineering Physics and Medical Physics, and proceeded with a PhD in Medical Physics at Umeå University. She then moved to Memorial Sloan Kettering Cancer Center in New York where for a postdoctoral fellowship followed by senior research. After 6 years in the US she returned to Sweden and Chalmers University of Technology where she now works.

Abstract: The field of medical image analysis is making great strides in the era of deep learning (DL), with a wide range of problems being addressed using such techniques. Two considerable limitations to the use of DL in medical imaging is the difficulty to utilize multimodal data for difficult forecasting tasks, and the oftentimes low level of explainability and missing uncertainty estimation of DL predictions. In my presentation, I will talk about how we have incorporated explainability in diagnostic and prognostic models, and survival modelling approaches to improve prognostication.

This is a hybrid event so you can also join via Zoom: https://zoom.us/j/99050467573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09

Meeting ID: 990 5046 7573 and Passcode: 617729

We look forward to your participation! If you are interested in getting involved and presenting your work, please email Ines Machado at im549@cam.ac.uk

For more information about this seminar series, see: https://www.integratedcancermedicine.org/research/cambridge-medai-seminar-series/