2025-03-04
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DigiLut: International Digital Pathology Challenge on Graft Rejection Detection Following Lung Transplantation Using Machine Learning
DigiLut: International Digital Pathology Challenge on Graft Rejection Detection Following Lung Transplantation Using Machine Learning
Arijana Bohr
Arijana Bohr
📍 Erlangen
👨🏽🔬 Researcher & PhD Candidate
🏫 Department Artificial Intelligence in Biomedical Engineering (AIBE), Lehrstuhl für Maschinelles Lernen und Datenanalytik
Dr. Emmanuelle Salin
Dr. Emmanuelle Salin
📍 Erlangen
👨🏽🔬 PostDoc and Group Leader
🏫 Department Artificial Intelligence in Biomedical Engineering (AIBE), Lehrstuhl für Maschinelles Lernen und Datenanalytik
Brief Summary
Brief Summary
Introduction to the DigiLut challenge: Detecting graft rejection regions-of-interest in Whole Slide Images (WSIs) using computer vision.
Presentation of the MaD lab team's approach (2nd place), from data preprocessing, to model training and post-processing.
Discussion on participation in an international challenge on machine learning for medicine.
Learning Objectives
Learning Objectives
Understand the basics of graft rejection detection
Learn about the challenges of preprocessing WSIs for computer vision
Discuss machine learning methods for WSIs, as well as techniques to handle data imbalance
Reflect on the participation in international challenges for medical data
Record
Record
Slides
Slides
MIRACUM_bohr_salin.pdfPage updated
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