Preserving Marker Specificity with Lightweight Channel-Independent Representation Learning
This work addresses the challenge of preserving marker specificity in multiplexed imaging data for biomedical research, offering a more efficient alternative to large-scale models.
The paper tackled the problem of deep learning models losing marker-specific information in multiplexed tissue imaging by proposing a lightweight channel-independent architecture, which achieved substantially stronger representations than early-fusion models, particularly for rare-cell discrimination, using a dataset with 145,000 cells and 49 markers.
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.