Preserving Marker Specificity with Lightweight Channel-Independent Representation Learning
To address the loss of protein marker specificity and poor rare-cell discrimination caused by early channel fusion in multiplexed tissue imaging data, this work proposes a lightweight self-supervised representation learning paradigm that explicitly preserves channel independence. Methodologically, we design CIM-S—a shallow, channel-independent model with only 5.5K parameters—incorporating contrastive self-supervised pretraining, an explicitly disentangled CNN architecture, customized spatial augmentations, and a linear evaluation protocol to rigorously maintain discriminative representations for each protein channel. Evaluated on 49- and 18-plex CODEX datasets, CIM-S significantly outperforms mainstream early-fusion CNNs and large foundation models, achieving a 12.6% absolute gain in rare-cell classification accuracy. It further demonstrates strong generalizability and high reproducibility across diverse experimental settings. This study provides the first empirical validation of the critical importance of both channel independence and architectural lightness for effective representation learning in multiplexed biological imaging.