The polyp segmentation technology based on computer-aided can effectively avoid the
deterioration of polyps and prevent colorectal cancer. To segment the polyp target precisely …

Clinically, automated polyp segmentation techniques have the potential to significantly
improve the efficiency and accuracy of medical diagnosis, thereby reducing the risk of …

Colorectal cancer (CRC) is one of the top three cancers in the world. The disease is initially
caused by the deterioration of polyps that are regarded as the predecessor of CRC. Many …

In recent years, there has been significant progress in polyp segmentation in white-light
imaging (WLI) colonoscopy images, particularly with methods based on deep learning (DL) …

Ultrasound breast tumour segmentation is a key step in computer-aided diagnosis and
provides an important basis for clinical diagnosis and analysis. Accurate segmentation of …

Automatic polyp segmentation is critical for early prevention and diagnosis of colorectal
cancer (CRC). However, diverse foreground appearance and complicated background …

Polyp segmentation is challenging due to the varying shapes and sizes, and low contrast,
resulting in blurred segmentation boundaries. To address this problem, we propose a multi …

Accuracy, generalized performance, and model size are critical considerations for designing
a real-time polyp segmentation model. However, existing techniques primarily focus on …

Purpose: Abnormal tissue detection is a prerequisite for medical image analysis and
computer-aided diagnosis and treatment. The use of neural networks (CNN) to achieve …

Vision transformers (ViTs) have recently outperformed convolutional neural networks
(CNNs) across a variety of deep learning tasks. In the field of orthopedic medicine, the …