MedAI: Transparency in Medical Image SegmentationVol. 1 No. 1 (2021)
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.
FishAIVol. 2 No. 2 (2022)
Sustainable Commercial Fishing is the second challenge at the Nordic AI Meet following the successful MedAI, which had a focus on medical image segmentation and transparency in machine learning (ML)-based systems. FishAI focuses on a new domain, namely, commercial fishing and how
to make it more sustainable with the help of machine learning.
A range of public available datasets is used to tackle three specific tasks. The first one is to predict fishing coordinates to optimize catching of specific fish, the second one is to create a report that can be used by experienced fishermen, and the third task is to make a sustainable fishing plan that provides a route for a week. The second and third task require to some extent explainable and interpretable models that can provide explanations. A development dataset is provided and all methods will be tested on a concealed test dataset and assessed by an expert jury
NMI 2022Vol. 2 No. 1 (2022)
MapAIVol. 2 No. 3 (2022)
MapAI: Precision in Building Segmentation is a competition
arranged with the Norwegian Artificial Intelligence Research
Consortium (NORA) in collaboration with Centre for Artificial Intelligence Research at the University of Agder (CAIR), the Norwegian Mapping Authority, AI:Hub, Norkart, and the Danish Agency for Data Supply and Infrastructure. The competition will be held in the fall of 2022. It will be concluded
at the Northern Lights Deep Learning conference focusing on
the segmentation of buildings using aerial images and laser
data. We propose two different tasks to segment buildings,
where the first task can only utilize aerial images, while the
second must use laser data (LiDAR) with or without aerial
images. Furthermore, we use IoU and Boundary IoU to
properly evaluate the precision of the models, with the latter
being an IoU measure that evaluates the results’ boundaries.
We provide the participants with a training dataset and keep
a test dataset for evaluation.