  Deep learning based medical imaging classification models usually suffer from
the domain shift problem, where the classification performance drops when
training data and real-world data differ in imaging equipment manufacturer,
image acquisition protocol, patient populations, etc. We propose Feature
Centroid Contrast Learning (FCCL), which can improve target domain
classification performance by extra supervision during training with
contrastive loss between instance and class centroid. Compared with current
unsupervised domain adaptation and domain generalization methods, FCCL performs
better while only requires labeled image data from a single source domain and
no target domain. We verify through extensive experiments that FCCL can achieve
superior performance on at least three imaging modalities, i.e. fundus
photographs, dermatoscopic images, and H & E tissue images.
