Deep learning has received extensive research interest in develo** new medical image
processing algorithms, and deep learning based models have been remarkably successful …

The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …

Traditional neuroimage analysis pipelines involve computationally intensive, time-
consuming optimization steps, and thus, do not scale well to large cohort studies with …

Image segmentation is an important task in many medical applications. Methods based on
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …

Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image
segmentation for a plethora of applications. Architectural innovations within F-CNNs have …

In a wide range of semantic segmentation tasks, fully convolutional neural networks (F-
CNNs) have been successfully leveraged to achieve the state-of-the-art performance …

Transformer-based models, capable of learning better global dependencies, have recently
demonstrated exceptional representation learning capabilities in computer vision and …

Detailed whole brain segmentation is an essential quantitative technique in medical image
analysis, which provides a non-invasive way of measuring brain regions from a clinical …

The alignment of images through deformable image registration is vital to clinical
applications (eg, atlas creation, image fusion, and tumor targeting in image-guided …

With the introduction of fully convolutional neural networks, deep learning has raised the
benchmark for medical image segmentation on both speed and accuracy, and different …