ImmuVis：面向影像质谱成像的超卷积基础模型
ImmuVis: Hyperconvolutional Foundation Model for Imaging Mass Cytometry
AI总结：
ImmuVis通过超卷积技术解决多通道成像中通道空间不固定的问题，实现对任意标记子集的高效处理，且在计算成本更低的情况下优于现有方法。
摘要：
我们提出了ImmuVis，一种针对影像质谱成像（IMC）的高效基础模型家族。IMC是一种高通量多通道成像技术，将分子标记测量作为图像通道处理，实现大规模空间组织分析。与自然图像不同，多通道成像缺乏固定通道空间，因为实际标记集在不同研究中变化，这违反了标准视觉骨干的核心假设。为此，ImmuVis引入了标记自适应的超卷积，通过学习的标记嵌入生成卷积核，使单个模型能够处理任意测量的标记子集而无需重新训练。我们预训练ImmuVis于迄今为止最大规模的数据集IMC17M（28个队列，24,405张图像，265个标记，超过17M个补丁），使用自监督的掩码重建。ImmuVis在虚拟染色和下游分类任务中优于现有最佳基线和消融实验，在计算成本显著低于基于变换器的替代方案的同时，且是唯一提供校准不确定性通过异方差似然目标的模型。这些结果使ImmuVis成为实际应用中IMC建模的实用框架。
英文摘要：
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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