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• W4386057777 abstract "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. We demonstrate on three different medical image modalities- CT, ultrasound, and MRI that our model is capable of producing several possible variants while capturing the frequencies of their occurrences. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks in terms of accuracy while preserving naturally occurring variation. We also propose a new metric to evaluate the diversity as well as the accuracy of segmentation predictions that aligns with the interest of clinical practice of collective insights. Implementation code: https://github.com/aimansnigdha/Ambiguous-Medical-Image-Segmentation-using-Diffusion-Models." @default.
• W4386057777 created "2023-08-23" @default.
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• W4386057777 date "2023-06-01" @default.
• W4386057777 modified "2023-10-06" @default.
• W4386057777 title "Ambiguous Medical Image Segmentation Using Diffusion Models" @default.
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• W4386057777 doi "https://doi.org/10.1109/cvpr52729.2023.01110" @default.
• W4386057777 hasPublicationYear "2023" @default.
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