The AttendSeg deep learning model performs semantic segmentation at an accuracy that is almost on-par with RefineNet while cutting down the number of parameters to 1.19 million.

Deep learning-based image segmentation model using an MRI-based convolutional neural network for physiological evaluation of the heart Wanni Xu 1,2,3 † Jianshe Shi 4 † Yunling Lin 5 Chao Liu 1,2,3 ...

Brain tumor segmentation is a critical task in diagnosing brain cancer and highly challenging due to the wide variety of the tumors’ location, size, and shape. Deep learning has been widely employed ...

Semantic segmentation based on deep learning has achieved impressive results in recent years, but these results are supported by a large amount of labeled data, which requires intensive annotation at ...

In this study, we proposed to use deep learning-based methods to automatically segment RAs in ultrasound images. The overall workflow of proposed framework was shown in Figure 3. The method consisted ...

Image semantic segmentation is ubiquitously used in scene understanding applications, such as AI Camera, which require high accuracy and efficiency. Deep learning has significantly advanced the ...

Thus, a simple algorithm based on a threshold pixel intensity value would perform poorly. The deep learning solution used for this problem was inspired by U-Net (shown below, image taken from the ...

Artificial intelligence, machine learning and deep learning are some of the biggest buzzwords around today. This guide provides a simple definition for deep learning that helps differentiate it ...