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 …

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

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

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

Feature pyramid networks (FPNs) are widely used in the existing deep detection models to
help them utilize multi-scale features. However, there exist two multi-scale feature fusion …

Medical image segmentation is an important and complex task in clinical practices, but the
widely used U-Net usually cannot achieve satisfactory performances in some clinical …

Digital pathology has played a key role in replacing glass slides with digital images,
enhancing various pathology workflows. Whole slide images are digitized pathological …

Accurate and automatic detection of tiny lesions in medical imaging plays a critical role in
comprehensive cancer diagnosis, staging, treatment, follow-up, and prognosis. Numerous …

Diagnosing pneumoconiosis is challenging because the lesions are not easily visible on
chest X-rays, and the images often lack clear details. Existing deep detection models utilize …

Analysis of X-ray images is one of the main tools to diagnose breast cancer. The ability to
quickly and accurately detect the location of masses from the huge amount of image data is …