通过自适应融合理解病理基础模型间的协同交互作用
Understanding Synergistic Interactions among Pathology Foundation Models via Adaptive Fusion
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中文总结 AI 辅助
本研究针对病理基础模型存在的表示偏差问题，提出AdaFusion自适应融合框架，在三个公共基准上验证其性能优于单个模型及其他融合方法，还可提供可解释的组织可视化。
中文摘要 AI 辅助
病理基础模型（PFMs）通过在大规模病理图像上进行自监督预训练，提供强大的图块级表示。然而，PFMs是在多样且通常不透明的数据、架构和目标选择下开发的，会引发潜在的表示偏差，限制了鲁棒性，并模糊了每个模型的专长。我们提出AdaFusion，这是一种轻量级自适应融合框架，它整合多个冻结PFMs的互补信号，通过（1）低维特征压缩和（2）样本条件门控模块对模型级（可选通道级）贡献进行重新加权。除了提高预测准确率外，AdaFusion还提供由贡献驱动的解释，其证据与特定模型偏好以及组织表型间的协同交互作用一致。我们在三个公共基准上评估AdaFusion，涵盖治疗反应预测、前列腺癌分级和空间基因表达推断。AdaFusion始终优于单个PFMs和其他融合基线，同时提供可解释的组织可视化，将模型偏好与形态学模式对齐。代码可在该https URL获取。
英文摘要
Pathology foundation models (PFMs) provide strong tile-level representations via self-supervised pre-training on large-scale pathology images. Yet, PFMs are developed under diverse and often opaque data, architecture, and objective choices, inducing latent representational biases that limit robustness and obscure what each model specialises in. We present AdaFusion, a lightweight adaptive fusion framework that integrates complementary signals from multiple frozen PFMs through (1) low-dimensional feature compression and (2) a sample-conditioned gating module that reweights model-wise (and optionally channel-wise) contributions. Beyond improving predictive accuracy, AdaFusion provides contribution-driven interpretation that offers evidence consistent with model-specific preferences and synergistic interactions across tissue phenotypes. We evaluate AdaFusion on three public benchmarks spanning treatment response prediction, prostate cancer grading, and spatial gene expression inference. AdaFusion consistently outperforms individual PFMs and other fusion baselines, while providing interpretable tissue visualisation which aligns model preferences with morphological patterns. Code is available at: https://github.com/xyx-98/PathoOracle.
发表机构
- School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）
- School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）
- Leicester Cancer Research Centre, University of Leicester（莱斯特大学莱斯特癌症研究中心）
- Department of Engineering Science, University of Oxford（牛津大学工程科学系）
- Nuffield Department of Medicine, University of Oxford（牛津大学纳菲尔德医学院）
- A*STAR, Singapore（新加坡科技研究局）
机构由 AI 辅助整理，请以论文原文为准。
