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.

本文提出了一种多头特征自适应模块，以促进源领域中的知识转移至目标领域，尤其适用于 WSI 分类；实验结果表明，我们的方法在多个数据集上均取得了最先进的性能，远优于从头开始训练的模型，证明了我们提出的方法的有效性。