这篇文章介绍了 THREADS，这是一个用于肿瘤病理学的新的基础模型，旨在解决计算病理学中数据稀疏性的挑战。THREADS 是一个通用编码器模型，通过多模态对比学习进行预训练，利用 下一代测序获得的分子图谱 来指导幻灯片表示学习。作者集合了迄今为止最广泛的多模态训练数据集 MBTG-47K（包含超过 47,000 个样本），并将 THREADS 的性能与多个基线模型进行了比较，显示其在 癌症分级、突变预测、IHC 状态预测、治疗反应和生存预测 等 54 项任务中的优越性能和鲁棒性。研究强调，这种分子引导的预训练策略能够捕获组织形态学的生物学和临床相关信息，并且 THREADS 模型在规模上比现有模型 更小且参数效率更高，但仍能提供更好的结果。References: Vaidya, A., et al. "Molecular-Driven Foundation Model for Oncologic Pathology. arXiv 2025." arXiv preprint arXiv:2501.16652.
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