Digital Pathology

Digital patologi

Digital pathology is a research branch at CMIV that truly lives up to the center’s ambitions to conduct world-leading imaging research directly addressing current challenges in clinical care. The research group builds on pioneering digitization efforts at Linköping University Hospital and adds innovative efforts in data collection and AI method development. CMIV takes the helm in turning powerful but frail AI algorithms into decision support solutions robust enough to face the clinical wilderness.

Digital pathology in a nutshell

Diagnostic pathology is of crucial importance for health care, especially cancer care. Pathologists analyze tissue, histology, and cell samples, cytology, from the patient. This knowledge is used to find the correct diagnosis and therapy. Digitization of the pathology images, whole-slide imaging, provides a possibility to increase both efficiency and quality of the diagnostic work. In order to fulfill this potential, cross-disciplinary research efforts are needed that can combine clinical expertise with knowledge in artificial intelligence (AI) and human-computer interaction.

Ground Truth for AI Models

The CMIV research agenda in digital pathology tackles several image analysis challenges. An important groundwork is to create systematically annotated collections of imaging data to be used as ground truth for training of AI models. CMIV has developed several large data collections, large also with international standards, that are shared for research purposes through the AIDA Data hub.
CMIV is also a leading partner of the EU project BIGPICTURE, creating an international data sharing platform with millions of slides.


Clinical applications of AI

A common ground for the CMIV digital pathology research projects is the focus on making AI-powered solutions work in the clinical setting. A key aspect is how to combine less-than-perfect AI with interactions from human users, at an effort level feasible for high-throughput clinical settings.
Our research agenda includes increasing diagnostic precision and efficiency, for several cancer types. We work within interactive machine learning to accelerate training and finetune during application. We also develop methodology and tools to validate and monitor AI solution performance in clinical production.

Research team

The CMIV pathology group consists both of medical and technical researchers from the university, pathologists and lab assistants from the clinical pathology department at the hospital, and researchers from industry.

The group is steadily expanding. If you think working in our group would interest you, please reach out to Adjunct Professor Claes Lundström (see below for contact).

Key Publications

Cover of publication ''
Joel Hedlund, Anders Eklund, Claes Lundström (2020)

Scientific Data , Vol.7 Continue to DOI

Cover of publication ''
Caroline Bivik Stadler, Martin Lindvall, Claes Lundström, Anna Boden, Karin Lindman, Jeronimo Rose, Darren Treanor, Johan Blomma, Karin Stacke, Nicolas Pinchaud, Martin Hedlund, Filip Landgren, Mischa Woisetschläger, Daniel Forsberg (2021)

Journal of digital imaging , Vol.34 , s.105-115 Continue to DOI

Cover of publication ''
Karin Skoglund, Jeronimo Rose, Martin Lindvall, Claes Lundström, Darren Treanor (2019)

Journal of Pathology Informatics , Vol.10 Continue to DOI

Cover of publication ''
Sylvia Asa, Anna Bodén, Darren Treanor, Sofia Jarkman, Claes Lundström, Liron Pantatnowitz (2019)

Journal of Pathology Informatics , Vol.10 Continue to DOI

Cover of publication ''
Martin Falk, Anders Ynnerman, Darren Treanor, Claes Lundström (2019)

IEEE Transactions on Visualization and Computer Graphics , Vol.25 , s.1008-1017 Continue to DOI