组织-基因组知识蒸馏用于组织病理全切片图像的癌症预后
Histo-Genomic Knowledge Distillation For Cancer Prognosis From Histopathology Whole Slide Images
AI总结：
提出G-HANet，通过跨模态关联分支和超注意力生存分支，在训练中蒸馏组织-基因组知识，提升仅基于WSI的癌症预后性能，并在TCGA基准上超越现有方法。
摘要：
组织-基因组多模态方法近年来已成为一种强大的范式，展现出改善癌症预后的巨大潜力。然而，与组织病理学成像不同，基因组测序在欠发达地区仍难以广泛获取，限制了这些多模态方法在临床环境中的应用。为解决这一问题，我们提出了一种新颖的基因组信息驱动的超注意力网络，称为G-HANet，该网络首次能够在训练期间有效蒸馏组织-基因组知识，以提升基于单模态全切片图像（WSI）的推理能力。与其他任务中的传统知识蒸馏方法（即教师-学生架构）相比，我们的端到端模型在训练效率和跨模态交互学习方面更具优势。具体而言，该网络包含跨模态关联分支（CAB）和超注意力生存分支（HSB）。通过从WSI中重建基因组数据，CAB有效蒸馏了功能基因型与形态表型之间的关联，并在特征空间中提供对基因表达谱的洞察。随后，HSB利用蒸馏得到的组织-基因组关联以及生成的基于形态学的权重，从组织病理学和基因组两个角度对患者进行超注意力建模，以改善癌症预后。我们在五个TCGA基准数据集上进行了广泛实验，结果表明G-HANet显著优于最先进的基于WSI的方法，并与基于基因组和多模态的方法达到竞争性性能。G-HANet有望被研究社区探索为一种有用工具，以解决当前癌症预后和精准肿瘤学背景下组织-基因组数据配对不足的瓶颈问题。
英文摘要：
Histo-genomic multi-modal methods have recently emerged as a powerful paradigm, demonstrating significant potential for improving cancer prognosis. However, genome sequencing, unlike histopathology imaging, is still not widely accessible in underdeveloped regions, limiting the application of these multi-modal approaches in clinical settings. To address this, we propose a novel Genome-informed Hyper-Attention Network, termed G-HANet, which is capable of effectively distilling the histo-genomic knowledge during training to elevate uni-modal whole slide image (WSI)-based inference for the first time. Compared with traditional knowledge distillation methods (i.e., teacher-student architecture) in other tasks, our end-to-end model is superior in terms of training efficiency and learning cross-modal interactions. Specifically, the network comprises the cross-modal associating branch (CAB) and hyper-attention survival branch (HSB). Through the genomic data reconstruction from WSIs, CAB effectively distills the associations between functional genotypes and morphological phenotypes and offers insights into the gene expression profiles in the feature space. Subsequently, HSB leverages the distilled histo-genomic associations as well as the generated morphology-based weights to achieve the hyper-attention modeling of the patients from both histopathology and genomic perspectives to improve cancer prognosis. Extensive experiments are conducted on five TCGA benchmarking datasets and the results demonstrate that G-HANet significantly outperforms the state-of-the-art WSI-based methods and achieves competitive performance with genome-based and multi-modal methods. G-HANet is expected to be explored as a useful tool by the research community to address the current bottleneck of insufficient histo-genomic data pairing in the context of cancer prognosis and precision oncology.
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