Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …

Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

Automated segmentation in medical image analysis is a challenging task that requires a
large amount of manually labeled data. However, most existing learning-based approaches …

Due to the difficulty in accessing a large amount of labeled data, semi-supervised learning is
becoming an attractive solution in medical image segmentation. To make use of unlabeled …

Semi-supervised learning has attracted much attention in medical image segmentation due
to challenges in acquiring pixel-wise image annotations, which is a crucial step for building …

The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …

Abstract Gross Target Volume (GTV) segmentation plays an irreplaceable role in
radiotherapy planning for Nasopharyngeal Carcinoma (NPC). Despite that Convolutional …

Transformers have made remarkable progress towards modeling long-range dependencies
within the medical image analysis domain. However, current transformer-based models …

Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …