arXiv Artificial Intelligence

Modelling Geographic Atrophy Progression using Implicit Neural Representations

1 day ago 12.08.2026 07:00 👁 2 views
Modelling Geographic Atrophy Progression using Implicit Neural Representations

Quick summary

arXiv:2608.10807v1 Announce Type: cross Abstract: Age-related Macular Degeneration (AMD) is the major cause of blindness in the Western world. Its late dry phase is characterised by irreversible atrophic areas, namely Geographic Atrophy (GA). Longitudinal Fundus Autofluorescence (FAF) image acquisitions are currently the main tool for assessing lesion growth over time at the image level. However, due to its highly individualised progression, the evolution of late AMD remains poorly understood. In this work, we propose using Implicit Neural Representations (INRs) to model GA progression at the

Key takeaways

  • arXiv:2608.10807v1 Announce Type: cross Abstract: Age-related Macular Degeneration (AMD) is the major cause of blindness in the Western world.
  • Its late dry phase is characterised by irreversible atrophic areas, namely Geographic Atrophy (GA).
  • Longitudinal Fundus Autofluorescence (FAF) image acquisitions are currently the main tool for assessing lesion growth over time at the image level.

Why it matters

“Modelling Geographic Atrophy Progression using Implicit Neural Representations” exposes the compute, energy and supply-chain layer behind model competition. Capacity shifts can influence model costs, service availability and the ability of smaller companies to compete.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗