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This is a Quantitative Study study.

October 3, 2025Open Access

A Probabilistic Segment Anything Model for Ambiguity-Aware Medical Image Segmentation

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Authors

TWTyler WardUniversity of Kentucky AIAbdullah-Al-Zubaer ImranUniversity of Kentucky

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Implication

Probabilistic SAM demonstrates improved segmentation diversity in medical imaging, highlighting the model's capability to manage annotation uncertainty.

Key Points

Probabilistic SAM produces diverse segmentation outputs, enhancing medical image analysis by accommodating expert disagreement.
Evaluations on the LIDC-IDRI dataset showed that the model significantly outperforms existing methods on uncertainty-aware metrics.
The model employs a variational objective for training, integrating latent variables that enhance flexibility and adaptability.
Incorporating a prior and posterior network, Probabilistic SAM effectively captures uncertainty in medical imaging tasks, facilitating better decision-making.

Cite This Study

Ward et al. (2025) studied this question.

synapsesocial.com/papers/68e02f3cf0e39f13e7fa2569 https://doi.org/10.48550/arxiv.2509.05809

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Implication

Probabilistic SAM demonstrates improved segmentation diversity in medical imaging, highlighting the model's capability to manage annotation uncertainty.

A Probabilistic Segment Anything Model for Ambiguity-Aware Medical Image Segmentation

Discussion

Member takes

Implication

Key Points

Cite This Study

A Probabilistic Segment Anything Model for Ambiguity-Aware Medical Image Segmentation

Discussion

Member takes

Implication

Key Points

Cite This Study

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