Despite its clinical utility, medical image segmentation (MIS) remains a
daunting task due to images' inherent complexity and variability. Vision
transformers (ViTs) have recently emerged as a promising solution to improve
MIS; however, they require larger training datasets than convolutional neural
networks. To overcome this obstacle, data-efficient ViTs were proposed, but
they are typically trained using a single source of data, which overlooks the
valuable knowledge that could be leveraged from other available datasets.
Naivly combining datasets from different domains can result in negative
knowledge transfer (NKT), i.e., a decrease in model performance on some domains
with non-negligible inter-domain heterogeneity. In this paper, we propose
MDViT, the first multi-domain ViT that includes domain adapters to mitigate
data-hunger and combat NKT by adaptively exploiting knowledge in multiple small
data resources (domains). Further, to enhance representation learning across
domains, we integrate a mutual knowledge distillation paradigm that transfers
knowledge between a universal network (spanning all the domains) and auxiliary
domain-specific branches. Experiments on 4 skin lesion segmentation datasets
show that MDViT outperforms state-of-the-art algorithms, with superior
segmentation performance and a fixed model size, at inference time, even as
more domains are added. Our code is available at
this https URL

本文提出了 MDViT，这是第一个包含领域适配器的多领域 ViT，通过自适应地利用多个小型数据资源 (领域) 中的知识来减轻数据饥饿和对抗 NKT，实现增强跨领域的表示学习。在 4 个皮肤病变分割数据集上的实验表明，MDViT 胜过了最先进的算法，在推理时具有更好的分割性能和固定模型大小，即使增加更多的领域。