Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …

Unsupervised domain adaptation without accessing expensive annotation processes of
target data has achieved remarkable successes in semantic segmentation. However, most …

Unsupervised domain adaptation has attracted appealing academic attentions by
transferring knowledge from labeled source domain to unlabeled target domain. However …

Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for
the target domain, in which only unlabeled and a small number of labeled data are …

Medical image segmentation is widely used in clinical diagnosis, and methods based on
convolutional neural networks have been able to achieve high accuracy. However, it is still …

Traditional supervised learning aims to train a classifier in the closed-set world, where
training and test samples share the same label space. In this paper, we target a more …

In unsupervised domain adaptation (UDA), a target-domain model is trained by the
supervised knowledge from a source domain. Although UDA has recently received much …

Unsupervised domain adaptation (UDA) aims to train a target classifier with labeled samples
from the source domain and unlabeled samples from the target domain. Classical UDA …

Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on
computed tomography (CT) images offers a great help to the estimation of intensive care unit …

In colonoscopy, the captured images are usually with low-quality appearance, such as non-
uniform illumination, low contrast, etc., due to the specialized imaging environment, which …