Gaussian representations outperform implicit methods in medical imaging

A new arXiv paper argues that explicit primitive representations, specifically Gaussian-based ones, are superior to Implicit Neural Representations for medical imaging tasks. The paper highlights that while implicit methods offer high fidelity, their computational costs and memory requirements are significant bottlenecks for high-resolution data. Gaussian representations, in contrast, demonstrate lower optimization times and memory usage while matching or exceeding reconstruction metrics in experiments on microscopy histology and computed tomography datasets. AI

IMPACT This research suggests a shift towards more computationally efficient and memory-friendly methods for medical imaging representation learning.

RANK_REASON The cluster contains an academic paper discussing new research findings and comparisons of different representation learning methods. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Computed Tomography
• Gaussian representations
• Implicit Neural Representations
• medical imaging
• microscopy histology
• Multi-Layer Perceptrons

• paper
• other

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