Transformer has already proven its ability to model long-distance dependencies. However,
medical images have strong local structures. Directly using Transformer to extract features …

Ultrasound imaging, characterized by low contrast, high noise, and interference from
surrounding tissues, poses significant challenges in lesion segmentation. To tackle these …

Transformer, born for long-range dependency establishment, has been widely studied as a
complementary of convolutional neural networks (CNNs) in medical image segmentation …

Rectal cancer is one of the most common malignant tumors of the digestive tract. Recently,
deep learning has attracted significant attention in computer-aided cancerous region …

Semantic segmentation is crucial for a wide range of downstream applications in remote
sensing, aiming to classify pixels in remote sensing images (RSIs) at the semantic level. The …

Predicting the drug–target binding affinity (DTA) is crucial in drug discovery, and an
increasing number of researchers are using artificial intelligence techniques to make such …

The primary goal of continual learning (CL) task in medical image segmentation field is to
solve the “catastrophic forgetting” problem, where the model totally forgets previously …

Despite the great potential in capturing long-range dependency, one rarely-explored
underlying issue of transformer in medical image segmentation is attention collapse, making …

The health of the spine is vital to the human body. However, the single-scale convolutional
network cannot fully capture the local detail features of the spine region. Meanwhile, the …

Cracks are a prevalent disease on pavement concrete materials. Timely assessment and
repair of concrete materials can significantly extend their service life. However, accurate …