Abstract
MedAI: Transparency in Medical Image Segmentation is a challenge held for the first time at the Nordic AI Meet that focuses on medical image segmentation and transparency in machine learning (ML)-based systems. We propose three tasks to meet specific gastrointestinal image segmentation challenges collected from experts within the field, including two separate segmentation scenarios and one scenario on transparent ML systems. The latter emphasizes the need for explainable and interpretable ML algorithms. We provide a development dataset for the participants to train their ML models, tested on a concealed test dataset.
Highlights
Medical image segmentation is a topic that has gained a lot of attention over the last few years
Colonoscopies are a perfect use case for medical image segmentation as they contain a great variety of findings that may be overlooked during the procedure
To promote more transparency in medical artificial intelligence (AI) research, we present the MedAI: Transparency in Medical Image Segmentation task that aims to develop automatic segmentation systems for segmenting findings in the gastrointestinal (GI) tract
Summary
UiT The Arctic University of Norway 4. Sahlgrenska University Hospital Mölndal, Västra Götaland Region, Sweden 10.
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