Withdrawn paper proposes Clifford-M for efficient fundus image classification

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

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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 →

• Clifford-M
• ODIR-5K
• RFMiD
• Yifeng Zheng

• paper
• other