AIRMEC: AI for refining the molecular endometrial cancer classification
Publications
CTRL K
GitHub
Notable work
HECTOR
Multimodal deep learning to predict distant recurrence-free probability from digitized H&E tumour slide and tumour stage.
Nature Medicine 2024
im4MEC
Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images.
Lancet Digital Health 2023
Team
Dr. Tjalling Bosse
Pathologist and endometrial cancer expert, Leiden University Medical Center
Prof. Dr. Viktor Koelzer
Pathologist and Digital Pathology Expert, University Hospital of Basel
Nanda Horeweg
Sarah Volinsky
PhD student, Department of Pathology, Leiden University Medical Center
Nikki van den Berg
PhD student, Department of Pathology, Leiden University Medical Center
Jurriaan Barkey Wolf
Software engineer, Department of Pathology, Leiden University Medical Center
Sonali Andani
PhD Student, Department of Computer Science, ETH Zurich
Maxime Lafarge
Lydia Schönpflug
PhD student, Department of Biomedical Engineering, University of Basel