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2023

Ambiguous Medical Image Segmentation using Diffusion Models

Rahman, Aimon, Valanarasu, Jeya Maria Jose, Hacihaliloglu, Ilker et al.

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Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks.

For the task of medical image segmentation, existing research on AI-based alternatives focuses more on developing models that can imitate the best individual rather than harnessing the power of expert groups.
In this paper, we introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights.
Our proposed model generates a distribution of segmentation masks by leveraging the inherent stochastic sampling process of diffusion using only minimal additional learning.

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