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 ...

Keywords: powdery mildew, cucumber leaf, convolutional neural network, image segmentation, deep-learning. Citation: Lin K, Gong L, Huang Y, Liu C and Pan J (2019) Deep Learning-Based Segmentation and ...

Deep learning-based semantic segmentation convolutional neural network (CNN) has millions of learnable parameters. However, depending on the complexity of the CNN, it takes hours to days to train the ...

We propose the deep hierarchical network (DHN) for the quantitative analysis of facial palsy. Facial palsy, also known as Bell’s palsy, is the most common type of facial nerve palsy that results in ...

A large dataset is needed when dealing with deep learning applications; usually an image segmentation task requires tens of thousands of images. Collecting such an amount of powerlines images is ...

Segment Routing (SR) is a source routing paradigm which is widely used in Traffic Engineering (TE). By using SR, a node steers a packet through an ordered list of instructions called segments. By some ...

The below figure, Deep Learning Frameworks, summarizes most of the popular open source deep network repositories in GitHub. The ranking is based on the number of stars awarded by developers in GitHub.