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Withdrawn paper proposes Clifford-M for efficient fundus image classification

By PulseAugur Editorial · [1 sources] ·

A research paper introduces Clifford-M, a novel lightweight backbone for fundus image classification that achieves competitive performance without explicit frequency decomposition. The model utilizes a Clifford-style rolling product for efficient cross-scale fusion and self-refinement. Tested on ODIR-5K, Clifford-M outperformed larger baselines with significantly fewer parameters, demonstrating its efficiency and effectiveness in capturing multi-scale structures for medical image analysis. AI

IMPACT Presents a new, efficient model architecture for medical image analysis that could influence future research in the field.

RANK_REASON This is a research paper presenting a novel model architecture for a specific image classification task.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Withdrawn paper proposes Clifford-M for efficient fundus image classification

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This is a research paper presenting a novel model architecture for a specific image classification task.
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yifeng Zheng ·

    Less is More in Semantic Space: Intrinsic Decoupling via Clifford-M for Fundus Image Classification

    arXiv:2603.20806v2 Announce Type: replace Abstract: Multi-label fundus diagnosis requires features that capture both fine-grained lesions and large-scale retinal structure. Many multi-scale medical vision models address this challenge through explicit frequency decomposition, but…

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