用于基于PET的肿瘤内异质性内容检索的弱监督病理信息表征学习
Weakly Supervised Pathology-Informed Representation Learning for PET-Based Content Retrieval of Intra-Tumour Heterogeneity
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中文总结 AI 辅助
提出用于基于PET的肿瘤内异质性内容检索的弱监督病理信息表征学习框架，采用师生训练策略，通过渐进式消融策略评估监督机制，实验表明该方法能提高检索性能，凸显肿瘤子区域对异质性的敏感性及类别摄取的独特性。
中文摘要 AI 辅助
我们提出了一种用于基于内容的医学图像检索的弱监督18F FDG PET表征学习框架，在训练期间使用苏木精和伊红（H&E）衍生信息，同时保留仅PET的推理。该方法在训练时使用H&E衍生信息并保持仅PET推理。采用师生训练策略学习PET肿瘤衍生的体素表征，在我们的食管癌测试案例中生成全局和热点条件嵌入以及肿瘤内异质性图。使用渐进式消融策略评估不同监督机制的贡献。通过包括平均精度均值、归一化折损累计增益和平均倒数排名等指标评估跨交叉验证折的检索性能。额外分析评估消融性能、通过扰动/删除实验评估热点忠实性、特定原型的PET摄取行为以及所学PET原型类别与选定组织学特征之间的间接患者水平一致性。与全局PET表征和传统PET基线相比，渐进引入病理信息监督和热点建模提高了PET检索性能。在消融阶梯中，PET热点条件表征始终比全局嵌入提供更强的检索，表明关注信息丰富的肿瘤子区域提高了对肿瘤内异质性的敏感性。组织病理学一致性进一步表明，所学类别不仅仅是高摄取PET区域；相反，它们在18F FDG摄取中表现出明显的异质性。
英文摘要
We propose a weakly supervised 18FFDG PET representation-learning framework for content based medical image retrieval, using H&E derived information during training while preserving PET-only inference. The proposed method was designed to use H&E derived information during training while maintaining PET only inference. A teacher student training strategy was used to learn the PET tumour derived voxel representations, from which global and hotspot conditioned embeddings were generated along with maps of intra tumour heterogeneity in our oesophegeal cancer test case. A progressive ablation strategy was used to evaluate the contribution of different supervision mechanisms. Retrieval performance was assessed across cross-validation folds using metrics including mean average precision, normalised discounted cumulative gain and mean reciprocal rank. Additional analyses evaluated ablation performance, hotspot faithfulness through perturbation/deletion experiments, prototype-specific PET uptake behaviour and indirect patient level concordance between learned PET prototype classes and selected histomic features. Progressive introduction of pathology informed supervision and hotspot modelling improved PET retrieval performance compared with global PET representations and conventional PET baselines. Across the ablation ladder, PET hotspot conditioned representations consistently provided stronger retrieval than global embeddings, indicating that focusing on informative tumour subregions improved sensitivity to intra tumour heterogeneity. Histopathology concordance further showed that the learned classes were not simply high uptake PET regions; instead, they demonstrated distinct heterogeneity in 18F FDG uptake.
