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arXiv · 2503.09826

Isolated Channel Vision Transformers: From Single-Channel Pretraining to Multi-Channel Finetuning

Wenyi Lian·Patrick Micke·Joakim Lindblad·Nataša Sladoje
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Abstract

Vision Transformers (ViTs) have achieved remarkable success in standard RGB image processing tasks. However, applying ViTs to multi-channel imaging (MCI) data, e.g., for medical and remote sensing applications, remains a challenge. In particular, MCI data often consist of layers acquired from different modalities. Directly training ViTs on such data can obscure complementary information and impair the performance. In this paper, we introduce a simple yet effective pretraining framework for large-scale MCI datasets. Our method, named Isolated Channel ViT (IC-ViT), patchifies image channels individually and thereby enables pretraining for multimodal multi-channel tasks. We show that this channel-wise patchifying is a key technique for MCI processing. More importantly, one can pretrain the IC-ViT on single channels and finetune it on downstream multi-channel datasets. This pretraining framework captures dependencies between patches as well as channels and produces robust feature representation. Experiments on various tasks and benchmarks, including JUMP-CP and CHAMMI for cell microscopy imaging, and So2Sat-LCZ42 for satellite imaging, show that the proposed IC-ViT delivers 4-14 percentage points of performance improvement over existing channel-adaptive approaches. Further, its efficient training makes it a suitable candidate for large-scale pretraining of foundation models on heterogeneous data. Our code is available at https://github.com/shermanlian/IC-ViT.

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Wenyi Lian, Patrick Micke, Joakim Lindblad, Nataša Sladoje. 2025-03-12. Isolated Channel Vision Transformers: From Single-Channel Pretraining to Multi-Channel Finetuning. https://arxiv.org/abs/2503.09826

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