Diversity-Promoting Ensemble for Medical Image Segmentation
- URL: http://arxiv.org/abs/2210.12388v1
- Date: Sat, 22 Oct 2022 08:47:25 GMT
- Title: Diversity-Promoting Ensemble for Medical Image Segmentation
- Authors: Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Andreea-Iuliana Miron
- Abstract summary: We propose a strategy to generate ensembles of different architectures for medical image segmentation.
To promote diversity, we select models with low Dice scores among each other.
Our empirical results show that DiPE surpasses both individual models as well as the ensemble creation strategy based on selecting the top scoring models.
- Score: 25.089517950882527
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Medical image segmentation is an actively studied task in medical imaging,
where the precision of the annotations is of utter importance towards accurate
diagnosis and treatment. In recent years, the task has been approached with
various deep learning systems, among the most popular models being U-Net. In
this work, we propose a novel strategy to generate ensembles of different
architectures for medical image segmentation, by leveraging the diversity
(decorrelation) of the models forming the ensemble. More specifically, we
utilize the Dice score among model pairs to estimate the correlation between
the outputs of the two models forming each pair. To promote diversity, we
select models with low Dice scores among each other. We carry out
gastro-intestinal tract image segmentation experiments to compare our
diversity-promoting ensemble (DiPE) with another strategy to create ensembles
based on selecting the top scoring U-Net models. Our empirical results show
that DiPE surpasses both individual models as well as the ensemble creation
strategy based on selecting the top scoring models.
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