ImmuVis让影像质谱模型能自适应任意标记组合，高效处理复杂组织图像。
ImmuVis: Hyperconvolutional Foundation Model for Imaging Mass Cytometry
- 用可学习的标记嵌入生成卷积核，支持任意标记组合输入
- 在1700万图像块上预训练，推理速度比变压器快得多
- 唯一能输出校准置信度的模型，适合临床精准分析
我们提出 ImmuVis，一类用于影像质谱（IMC）的高效基础模型。IMC 是一种高通量多路成像技术，将分子标记测量作为图像通道，实现大规模空间组织图谱分析。与自然图像不同，多路成像没有固定的通道空间，因实际标记集在不同研究中变化，违背了标准视觉骨干网络的核心假设。为解决此问题，ImmuVis 引入标记自适应超卷积，从学习到的标记嵌入生成卷积核，使单一模型可在不重新训练的情况下处理任意测量标记子集。我们在迄今最大的数据集 IMC17M（28 个队列，24,405 张图像，265 个标记，超过 1700 万图像块）上进行自监督掩码重建预训练。ImmuVis 在虚拟染色和下游分类任务中显著优于现有基线与消融实验，且计算成本远低于基于变压器的方案，并唯一通过异方差似然目标提供校准不确定性。这些结果使 ImmuVis 成为真实世界 IMC 建模的实用框架。
原文摘要 · Abstract (English)
We present ImmuVis, a family of efficient foundation models for imaging mass cytometry (IMC), a high-throughput multiplex imaging technology that handles molecular marker measurements as image channels and enables large-scale spatial tissue profiling. Unlike natural images, multiplex imaging lacks a fixed channel space, as real-world marker sets vary across studies, violating a core assumption of standard vision backbones. To address this, ImmuVis introduces marker-adaptive hyperconvolutions that generate convolutional kernels from learned marker embeddings, enabling a single model to operate on arbitrary measured marker subsets without retraining. We pretrain ImmuVis on the largest dataset to date, IMC17M (28 cohorts, 24,405 images, 265 markers, over 17M patches), using self-supervised masked reconstruction. ImmuVis outperforms state-of-the-art baselines and ablations in virtual staining and downstream classification tasks at substantially lower compute cost than transformer-based alternatives, and is the sole model that provides calibrated uncertainty via a heteroscedastic likelihood objective. These results position ImmuVis as a practical framework for real-world IMC modeling.
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