Related papers: Flow Stochastic Segmentation Networks

Flow Stochastic Segmentation Networks

URL: http://arxiv.org/abs/2507.18838v1
Date: Thu, 24 Jul 2025 22:26:28 GMT
Title: Flow Stochastic Segmentation Networks
Authors: Fabio De Sousa Ribeiro, Omar Todd, Charles Jones, Avinash Kori, Raghav Mehta, Ben Glocker,
Abstract summary: Flow-SSN is a generative segmentation model family featuring discrete-time autoregressive and modern continuous-time flow variants.<n>We apply Flow-SSNs to challenging medical imaging benchmarks and achieve state-of-the-art results.
Score: 16.173163796354675
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We introduce the Flow Stochastic Segmentation Network (Flow-SSN), a generative segmentation model family featuring discrete-time autoregressive and modern continuous-time flow variants. We prove fundamental limitations of the low-rank parameterisation of previous methods and show that Flow-SSNs can estimate arbitrarily high-rank pixel-wise covariances without assuming the rank or storing the distributional parameters. Flow-SSNs are also more efficient to sample from than standard diffusion-based segmentation models, thanks to most of the model capacity being allocated to learning the base distribution of the flow, constituting an expressive prior. We apply Flow-SSNs to challenging medical imaging benchmarks and achieve state-of-the-art results. Code available: https://github.com/biomedia-mira/flow-ssn.

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