MedAI: Transparency in Medical Image Segmentation

Authors

  • Steven Hicks SimulaMet
  • Debesh Jha SimulaMet
  • Vajira Thambawita SimulaMet
  • Pål Halvorsen SimulaMet
  • Bjørn-Jostein Singstad Oslo University Hospital
  • Sachin Gaur University of Oslo
  • Klas Pettersen NORA
  • Morten Goodwin University of Agder
  • Sravanthi Parasa Swedish Medical Center
  • Thomas de Lange Sahlgrenska University Hospital Molndal
  • Michael Riegler SimulaMet

DOI:

https://doi.org/10.5617/nmi.9140

Keywords:

challenge, medical, segmentation, machine learning, artificial intelligence, transparency

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.

Downloads

Published

2021-11-01