[2507.22418] Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching

Computer Science > Computer Vision and Pattern Recognition arXiv:2507.22418 (cs) [Submitted on 30 Jul 2025 (v1), last revised 7 Apr 2026 (this version, v3)] Title:Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching Authors:Phi Van Nguyen, Ngoc Huynh Trinh, Duy Minh Lam Nguyen, Phu Loc Nguyen, Quoc Long Tran View a PDF of the paper titled Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching, by Phi Van Nguyen and 4 other authors View PDF HTML (experimental) Abstract:Quantifying aleatoric uncertainty in medical image segmentation is critical since it is a reflection of the natural variability observed among expert annotators. A conventional approach is to model the segmentation distribution using the generative model, but current methods limit the expression ability of generative models. While current diffusion-based approaches have demonstrated impressive performance in approximating the data distribution, their inherent stochastic sampling process and inability to model exact densities limit their effectiveness in accurately capturing uncertainty. In contrast, our proposed method leverages conditional flow matching, a simulation-free flow-based generative model that learns an exact density, to produce highly accurate segmentation results. By guiding the flow model on the input image and sampling multiple data points, our approach synthesizes segmentation samples whose pixel-wise variance reliably reflects the underlying da...