Development of AI tools for improved analysis of tissue section images from cancers in a cross-disciplinary environment

Name: Anders Isaksson

Project title: Development of AI tools for improved analysis of tissue section images from cancers in a cross-disciplinary environment

Department: Department of Medical Sciences

Area of research: Molecular oncology

project summary

In a pilot project funded by Cancerfonden we address the question of why some endometrial carcinomas appear to evade the anti-tumor response launched by the immune system. We test a number of specific hypotheses by searching for associations between molecular/spatial patterns in tissue sections and patient outcome. In order to reach this goal we train and apply deep learning algorithms to identify different spatial cell patterns in Hematoxylin & Eosin (H&E) stained tissue sections. As an example we identify the pattern of Tumor Infiltrating Lymphocytes (TILs).

Using the unique data sets available to us, as well as the outcome of our automated image analysis and TIL identification, we detect patterns associated to prognosis. We also search for novel molecular mechanisms underlying these patterns. In summary this program aims at providing improved prognostication with more certain and informative prognoses  for patients and clinicians.

What do you look forward to the most during your sabbatical?

I am looking forward to discussing the common challenges we face while developing and applying AI methods to new applications.