Transferring prior knowledge from a source domain to the same or similar target domain can greatly enhance the performance of models on the target domain. However, it is challenging to directly leverage the knowledge from the source domain due to task discrepancy and domain shift. To bridge the gaps between different tasks and domains, we propose a Multi-Head Feature Adaptation module, which projects features in the source feature space to a new space that is more similar to the target space. Knowledge transfer is particularly important in Whole Slide Image (WSI) classification since the number of WSIs in one dataset might be too small to achieve satisfactory performance. Therefore, WSI classification is an ideal testbed for our method, and we adapt multiple knowledge transfer methods for WSI classification. The experimental results show that models with knowledge transfer outperform models that are trained from scratch by a large margin regardless of the number of WSIs in the datasets, and our method achieves state-of-the-art performances among other knowledge transfer methods on multiple datasets, including TCGA-RCC, TCGA-NSCLC, and Camelyon16 datasets.

翻译：从源领域向相同或相似目标领域迁移先验知识，可显著提升模型在目标领域的性能。然而，由于任务差异和领域偏移，直接利用源领域知识存在挑战。为弥合不同任务与领域间的鸿沟，我们提出多头特征适配模块，将源特征空间中的特征投影至与目标空间更相似的新空间。知识迁移对全切片图像（WSI）分类尤为重要，因其单个数据集中的WSI数量可能过少而难以取得理想性能。因此，WSI分类成为验证本方法的理想试验场，我们针对WSI分类适配了多种知识迁移方法。实验结果表明，无论数据集中WSI数量多寡，采用知识迁移的模型性能均远超从头训练的模型；且在TCGA-RCC、TCGA-NSCLC及Camelyon16等多组数据集上，本方法在各类知识迁移方法中取得了最先进的性能。