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arXiv AI By Yi-Hui Shen, Tie-Qiang Li

Learning Spectral Allocation: A Fractional Diffusion Framework for Adaptive Volumetric Segmentation

Read the original on arXiv AI →

The paper introduces FHEAT, a fractional diffusion operator derived from the discrete cosine transform, to learn how much spectral mixing each stage of a 3D medical segmentation network should perform. By reparameterizing the operator with a semigroup time, the authors enable the optimizer to decide whether global mixing is needed, resulting in a lightweight U‑shaped architecture (Light‑UNETR) paired with a Kolmogorov‑Arnold mixer (KAN3D). In semi‑supervised experiments, FHEAT‑Seg achieves state‑of‑the‑art Dice scores while dramatically reducing FLOPs through learned spectral sparsification.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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