Research in artificial intelligence for radiology and radiotherapy has recently become
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …

Autonomous driving has been among the most popular and challenging topics in the past
few years. Among all modules in autonomous driving, High-definition (HD) map has drawn …

Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …

Although current salient object detection (SOD) works have achieved significant progress,
they are limited when it comes to the integrity of the predicted salient regions. We define the …

Medical image segmentation has seen significant improvements with transformer models,
which excel in grasping far-reaching contexts and global contextual information. However …

Deep-learning methods for auto-segmenting brain images either segment one slice of the
image (2D), five consecutive slices of the image (2.5 D), or an entire volume of the image …

Prominent solutions for medical image segmentation are typically tailored for automatic or
interactive setups posing challenges in facilitating progress achieved in one task to another …

Over the past few years, the computer vision domain has evolved and made a revolutionary
transition from human-engineered features to automated features to address challenging …

Lung cancer is among the most common and deadliest cancers with a low 5-year survival
rate. Timely diagnosis of lung cancer is, therefore, of paramount importance as it can save …

Medical image segmentation has been so far achieving promising results with Convolutional
Neural Networks (CNNs). However, it is arguable that in traditional CNNs, its pooling layer …