PAINT: Pathology-Aware Integrated Next-Scale Transformation for Virtual Immunohistochemistry

• URL: http://arxiv.org/abs/2601.16024v1
• Date: Thu, 22 Jan 2026 14:49:30 GMT
• Title: PAINT: Pathology-Aware Integrated Next-Scale Transformation for Virtual Immunohistochemistry
• Authors: Rongze Ma, Mengkang Lu, Zhenyu Xiang, Yongsheng Pan, Yicheng Wu, Qingjie Zeng, Yong Xia,
• Abstract summary: Virtualchemistry aims to computationally synthesize molecular staining patterns from routine Hematoxylin and Eosin (H&E) images.<n>We propose Pathology-Aware Integrated Next-Scale Transformation (PAINT), a visual autoregressive framework that reformulates the synthesis process as a structure-first conditional generation task.
• Score: 17.230315436967356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Virtual immunohistochemistry (IHC) aims to computationally synthesize molecular staining patterns from routine Hematoxylin and Eosin (H\&E) images, offering a cost-effective and tissue-efficient alternative to traditional physical staining. However, this task is particularly challenging: H\&E morphology provides ambiguous cues about protein expression, and similar tissue structures may correspond to distinct molecular states. Most existing methods focus on direct appearance synthesis to implicitly achieve cross-modal generation, often resulting in semantic inconsistencies due to insufficient structural priors. In this paper, we propose Pathology-Aware Integrated Next-Scale Transformation (PAINT), a visual autoregressive framework that reformulates the synthesis process as a structure-first conditional generation task. Unlike direct image translation, PAINT enforces a causal order by resolving molecular details conditioned on a global structural layout. Central to this approach is the introduction of a Spatial Structural Start Map (3S-Map), which grounds the autoregressive initialization in observed morphology, ensuring deterministic, spatially aligned synthesis. Experiments on the IHC4BC and MIST datasets demonstrate that PAINT outperforms state-of-the-art methods in structural fidelity and clinical downstream tasks, validating the potential of structure-guided autoregressive modeling.

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