The loss function, also known as cost function, is used for training a neural network or other
machine learning models. Over the past decade, researchers have designed many loss …

Skin cancer is a major public health problem that could benefit from computer-aided
diagnosis to reduce the burden of this common disease. Skin lesion segmentation from …

Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

Multi-modal data abounds in biomedicine, such as radiology images and reports.
Interpreting this data at scale is essential for improving clinical care and accelerating clinical …

Automatic segmentation methods are an important advancement in medical image analysis.
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …

Sleep disorders affect a large portion of the global population and are strong predictors of
morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence …

Scene understanding of high resolution aerial images is of great importance for the task of
automated monitoring in various remote sensing applications. Due to the large within-class …

The Hausdorff Distance (HD) is widely used in evaluating medical image segmentation
methods. However, the existing segmentation methods do not attempt to reduce HD directly …

Medical image segmentation is an important step in medical image analysis. With the rapid
development of a convolutional neural network in image processing, deep learning has …

Deep-learning has proved in recent years to be a powerful tool for image analysis and is
now widely used to segment both 2D and 3D medical images. Deep-learning segmentation …