# Less is More in Semantic Space: Intrinsic Decoupling via Clifford-M for Fundus Image Classification Source: [Less is More in Semantic Space: Intrinsic Decoupling via Clifford-M for Fundus Image Classification]() Collection index: [llms.txt]() Data updated: 2026-04-29T05:00:19.306Z Authors: Yifeng Zheng Published: 2026-03-21T13:00:05.000Z ## 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 our ablation studies show that such heuristics provide limited benefit in this setting: replacing the proposed simple dual-resolution stem with Octave Convolution increased parameters by 35% and computation by a 2.23-fold increase in computation; without improving mean accuracy, while a fixed wavelet-based variant performed substantially worse. Motivated by these findings, we propose Clifford-M, a lightweight backbone that replaces both feed-forward expansion and frequency-splitting modules with sparse geometric interaction. The model is built on a Clifford-style rolling product that jointly captures alignment and structural variation with linear complexity, enabling efficient cross-scale fusion and self-refinement in a compact dual-resolution architecture. Without pre-training, Clifford-M achieves a mean AUC-ROC of 0.8142 and a mean macro-F1 (optimal threshold) of 0.5481 on ODIR-5K using only 0.85M parameters, outperforming substantially larger mid-scale CNN baselines under the same training protocol. When evaluated on RFMiD without fine-tuning, it attains 0.7425 +/- 0.0198 macro AUC and 0.7610 +/- 0.0344 micro AUC, indicating reasonable robustness to cross-dataset shift. These results suggest that competitive and efficient fundus diagnosis can be achieved without explicit frequency engineering, provided that the core feature interaction is designed to capture multi-scale structure directly. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Ocular Disease Recognition on ODIR-5K](): Ocular Disease Recognition; ODIR-5k; AUC-ROC: 81.42. - [Multi-label fundus diagnosis on ODIR-5K](): Multi-label fundus diagnosis; ODIR-5k; AUC-ROC: 81.42. ## Original sources and implementation [Original paper]() [PDF]()