With the improvement of hardware computing power and the development of deep learning
algorithms, a revolution of" artificial intelligence (AI)+ medical image" is taking place …

Existing self-supervised medical image segmentation usually encounters the domain shift
problem (ie, the input distribution of pre-training is different from that of fine-tuning) and/or …

Automatic medical image detection aims to utilize artificial intelligence techniques to detect
lesions in medical images accurately and efficiently, which is one of the most important tasks …

Although the existing deep supervised solutions have achieved some great successes in
medical image segmentation, they have the following shortcomings;(i) semantic difference …

A common problem in the field of deep-learning-based low-level vision medical images is
that most of the research is based on single task learning (STL), which is dedicated to …

Pre-processing is widely applied in medical image analysis to remove the interference
information. However, the existing pre-processing solutions mainly encounter two …

Data augmentation is widely applied to medical image analysis tasks in limited datasets with
imbalanced classes and insufficient annotations. However, traditional augmentation …

Uterine segmentation of endometrial cancer MR images can be a valuable diagnostic tool
for gynecologists. However, uterine segmentation based on deep learning relies on artificial …

Background and objective: The diagnosis of BI-RADS category 4 breast lesion is difficult
because its probability of malignancy ranges from 2% to 95%. For BI-RADS category 4 …

Although existing deep reinforcement learning-based approaches have achieved some
success in image augmentation tasks, their effectiveness and adequacy for data …