Medical image segmentation faces current challenges in effectively extracting and fusing
long-distance and local semantic information, as well as mitigating or eliminating semantic …

Multi-phase liver contrast-enhanced computed tomography (CECT) images convey the
complementary multi-phase information for liver tumor segmentation (LiTS), which are …

Monocular depth estimation (MDE), as one of the fundamental tasks of computer vision,
plays important roles in downstream applications such as virtual reality, 3D reconstruction …

Existing approaches to 3D medical image segmentation can be generally categorized into
convolution-based or transformer-based methods. While convolutional neural networks …

Automatic polyp segmentation at colonoscopy plays an important role in the early diagnosis
and surgery of colorectal cancer. However, the diversity of polyps in different images greatly …

Background: Segmenting colorectal polyps presents a significant challenge due to the
diverse variations in their size, shape, texture, and intricate backgrounds. Particularly …

Accurate segmentation of honeycomb lung lesions from lung CT images plays a crucial role
in the diagnosis and treatment of various lung diseases. However, the availability of …

Medical image segmentation is a compelling fundamental problem and an important
auxiliary tool for clinical applications. Recently, the Transformer model has emerged as a …

A histopathological image is a microscopic image applied to examine cellular and tissue
structures and identify any abnormalities or disease processes. Histopathological image …

Convolutional architectures have demonstrated remarkable success in various vision tasks,
offering efficient learning through their inherent induction bias. However, they might be …