Apply Energy-Mamba to medical image segmentation

Extend Energy-Mamba to medical image segmentation and determine whether its physics-informed state-space dynamics improve segmentation performance and feature preservation.

Background

The reported experiments address medical image classification only. Applying Energy-Mamba to segmentation is explicitly identified as an open future direction, requiring assessment of how the potential-energy constraint operates in dense prediction settings where precise spatial boundaries and pixel- or voxel-level representations are central.

References

Several directions remain open for future investigation, including the extension of Energy-Mamba to 3D volumetric medical data, the extension to other tasks such as segmentation, the exploration of alternative energy function architectures, and a systematic ablation of the interplay between the kinetic and potential branches across different imaging modalities and dataset scales.

— Energy-Mamba: A Physics-Constrained State-Space Model for Medical Image Classification  (2608.19813 - Mabrok et al., 20 Aug 2026) in Conclusion