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SUMMARY:Cambridge MedAI Seminar - January 2026 - Ida Häggström\, Associa
 te Professor\, Unit of Computer Vision and Medical Image Analysis\, Chalme
 rs University of Technology\, Sweden
DTSTART:20260127T114500Z
DTEND:20260127T130000Z
UID:TALK243595@talks.cam.ac.uk
CONTACT:Hannah Clayton
DESCRIPTION:Join us for the *Cambridge AI in Medicine Seminar Series*\, ho
 sted by the *Cancer Research UK Cambridge Centre* and the *Department of R
 adiology at Addenbrooke's*. This series brings together leading experts to
  explore cutting-edge AI applications in healthcare - from disease diagnos
 is to drug discovery. It's a unique opportunity for researchers\, practiti
 oners\, and students to stay at the forefront of AI innovations and engage
  in discussions shaping the future of AI in healthcare.\n\nThis month's se
 minar will be held on *Tuesday 27 January 2025\, 12-1pm at the Jeffrey Che
 ah 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:\n\n*_Explainability and 
 forecasting in medical imaging_ - Ida Häggström\, Associate Professor\, 
 Unit of Computer Vision and Medical Image Analysis\, Chalmers University o
 f Technology\, Sweden*\n\nIda is an Associate Professor in the Computer Vi
 sion and Medical Image Analysis group at Chalmers University of Technology
 \, in Gothenburg\, Sweden\, working with machine and deep learning techniq
 ues for medical image analysis. She collaborates closely with clinical res
 earchers on projects to diagnose\, predict and prognosticate different dis
 eases\, 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 Ce
 nter in New York where for a postdoctoral fellowship followed by senior re
 search. After 6 years in the US she returned to Sweden and Chalmers Univer
 sity of Technology where she now works.\n\n*Abstract*: The field of medica
 l 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 diffi
 culty 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 inc
 orporated explainability in diagnostic and prognostic models\, and surviva
 l modelling approaches to improve prognostication.\n\n\nThis is a hybrid e
 vent so you can also join via Zoom:\nhttps://zoom.us/j/99050467573?pwd=UE5
 OdFdTSFdZeUtIcU1DbXpmdlNGZz09\n\nMeeting ID: 990 5046 7573 and Passcode: 6
 17729\n\nWe look forward to your participation! If you are interested in g
 etting involved and presenting your work\, please email Ines Machado at im
 549@cam.ac.uk\n\nFor more information about this seminar series\, see: htt
 ps://www.integratedcancermedicine.org/research/cambridge-medai-seminar-ser
 ies/
LOCATION:Jeffrey Cheah Biomedical Centre (Main Lecture Theatre)\, Universi
 ty of Cambridge
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