Preserving Marker Specificity with Lightweight Channel-Independent Representation Learning

Simon Gutwein, Arthur Longuefosse, Jun Seita, Sabine Taschner-Mandl, Roxane Licandro

Keywords: Multiplex Imaging, Representation learning, Channel-Separated Architecture, Phenotyping

TL;DR: This study demonstrates that marker independence is a key inductive bias in multiplex imaging, allowing compact channel-independent models to reach foundation-level self-supervised performance and yield more specific marker-driven phenotyping.

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 markers and reduced 18 markers 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

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Application: Histopathology

Registration Requirement: Yes

Reproducibility: https://github.com/SimonBon/CIM-S

Visa & Travel: No

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

Submission Number: 36