Preserving Marker Specificity with Lightweight Channel-Independent Representation Learning

TL;DR

This paper demonstrates that lightweight, channel-independent neural network architectures outperform traditional early-fusion models in preserving marker-specific information in multiplexed tissue imaging, especially for rare-cell discrimination.

Contribution

The study introduces a novel shallow channel-independent model (CIM-S) that effectively preserves marker independence and improves representation learning in multiplexed imaging data.

Findings

Channel-independent architectures outperform early-fusion CNNs in marker-specific tasks.

CIM-S achieves strong performance with significantly fewer parameters.

Results are consistent across different datasets and self-supervised frameworks.

Abstract

Multiplexed tissue imaging measures dozens of protein markers per cell, yet most deep learning models still apply early channel fusion, assuming shared structure across markers. We investigate whether preserving marker independence, combined with deliberately shallow architectures, provides a more suitable inductive bias for self-supervised representation learning in multiplex data than increasing model scale. Using a Hodgkin lymphoma CODEX dataset with 145,000 cells and 49 markers, we compare standard early-fusion CNNs with channel-separated architectures, including a marker-aware baseline and our novel shallow Channel-Independent Model (CIM-S) with 5.5K parameters. After contrastive pretraining and linear evaluation, early-fusion models show limited ability to retain marker-specific information and struggle particularly with rare-cell discrimination. Channel-independent architectures, and CIM-S in particular, achieve substantially stronger representations despite their compact size. These findings are consistent across multiple self-supervised frameworks, remain stable across augmentation settings, and are reproducible across both the 49-marker and reduced 18-marker settings. These results show that lightweight, channel-independent architectures can match or surpass deep early-fusion CNNs and foundation models for multiplex representation learning. Code is available at https://github.com/SimonBon/CIM-S.