(* U-Net architecture is a deep learning image segmentation architecture introduced by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015. Its U shape design consists of two parts. The left ...

In this study, a deep learning-based model with an encoder–decoder structure was proposed for MRI image segmentation and application to glioma. The model obtained in the RegNet design space was ...

Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, ...

Additionally, a novel deep learning methodology based on a dual stream encoder and decoder architecture is proposed for automatically segmenting panoramic images. The evaluation is carried out on 1000 ...

Spikesegnet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging. Plant Methods 16, 1–20. doi: 10.1186 ...

The encoder captures spatial features, while the decoder upscales them to create a segmentation map. U-Net is particularly effective in identifying objects in complex, noisy images. Mask R-CNN : An ...

PyTorch implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation - vinceecws/SegNet_PyTorch ...