AdvDINO: Domain-Adversarial Self-Supervised Representation Learning for Spatial Proteomics

• URL: http://arxiv.org/abs/2508.04955v1
• Date: Thu, 07 Aug 2025 00:51:54 GMT
• Title: AdvDINO: Domain-Adversarial Self-Supervised Representation Learning for Spatial Proteomics
• Authors: Stella Su, Marc Harary, Scott J. Rodig, William Lotter,
• Abstract summary: Self-supervised learning (SSL) has emerged as a powerful approach for learning visual representations without manual annotations.<n>We present AdvDINO, a domain-adversarial self-supervised learning framework that integrates a gradient reversal layer into the DINOv2 architecture to promote domain-invariant feature learning.
• Score: 0.42855555838080833
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised learning (SSL) has emerged as a powerful approach for learning visual representations without manual annotations. However, the robustness of standard SSL methods to domain shift -- systematic differences across data sources -- remains uncertain, posing an especially critical challenge in biomedical imaging where batch effects can obscure true biological signals. We present AdvDINO, a domain-adversarial self-supervised learning framework that integrates a gradient reversal layer into the DINOv2 architecture to promote domain-invariant feature learning. Applied to a real-world cohort of six-channel multiplex immunofluorescence (mIF) whole slide images from non-small cell lung cancer patients, AdvDINO mitigates slide-specific biases to learn more robust and biologically meaningful representations than non-adversarial baselines. Across $>5.46$ million mIF image tiles, the model uncovers phenotype clusters with distinct proteomic profiles and prognostic significance, and improves survival prediction in attention-based multiple instance learning. While demonstrated on mIF data, AdvDINO is broadly applicable to other imaging domains -- including radiology, remote sensing, and autonomous driving -- where domain shift and limited annotated data hinder model generalization and interpretability.

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