Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

Accurate segmentation of topological tubular structures, such as blood vessels and roads, is
crucial in various fields, ensuring accuracy and efficiency in downstream tasks. However …

Medical image segmentation is an essential process to assist clinics with computer-aided
diagnosis and treatment. Recently, a large amount of convolutional neural network (CNN) …

Coronary artery segmentation is a crucial prerequisite for computer-aided diagnosis of
coronary artery disease (CAD). However, this task remains challenging due to the intricate …

Hypopharyngeal cancer (HPC) is a rare disease. Therefore, it is a challenge to automatically
segment HPC tumors and metastatic lymph nodes (HPC risk areas) from medical images …

Medical image segmentation is a crucial task in computer-aided diagnosis. Although
convolutional neural networks (CNNs) have made significant progress in the field of medical …

A popular benchmark for intra-patient lung registration is provided by the DIR-LAB
COPDgene dataset consisting of large-motion in-and expiratory breath-hold CT pairs. This …

Unmanned Aerial Vehicles (UAVs) combined with target detection algorithms can enhance
the detection of road cracks. In response to the challenges presented by complex crack …

The morphologies of vessel-like structures, such as blood vessels and nerve fibres, play
significant roles in disease diagnosis, eg, Parkinson's disease. Although deep network …

Accurate segmentation of hepatic vessel is significant for the surgeons to design the
preoperative planning of liver surgery. In this paper, a sequence-based context-aware …