The loss function is an important component in deep learning-based segmentation methods.
Over the past five years, many loss functions have been proposed for various segmentation …

Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …

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 …

Image Segmentation has been an active field of research as it has a wide range of
applications, ranging from automated disease detection to self driving cars. In the past five …

Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …

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 …

Autonomous robotic systems and self driving cars rely on accurate perception of their
surroundings as the safety of the passengers and pedestrians is the top priority. Semantic …

We propose a generalized focal loss function based on the Tversky index to address the
issue of data imbalance in medical image segmentation. Compared to the commonly used …

Widely used loss functions for convolutional neural network (CNN) segmentation, eg, Dice
or cross-entropy, are based on integrals (summations) over the segmentation regions …

In semi-supervised medical image segmentation, most previous works draw on the common
assumption that higher entropy means higher uncertainty. In this paper, we investigate a …