GitHub

Superpixel-based Graph Convolutional Network for Semantic Segmentation

@article{jungsuperpixel, title={Superpixel-based Graph Convolutional Network for Semantic Segmentation}, author={Jung, Hoin and Park, Seong Yeon and Yang, Su and Kim, Jin} } Thie repository is an ...
IEEE

CNN-G: Convolutional Neural Network Combined With Graph for Image Segmentation With Theoretical Analysis

Abstract: Deep convolutional neural network (CNN), although recognized to be considerably successful in its application to semantic segmentation, is inadequate for extracting the overall structure ...
Nature

Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image analysis using MRI images

The undesirable development of cells originates in a tumour in the brain that is named a brain tumour, considering the cells distribute and mature uncontrolled 1. The tumour in the brain might be ...
Frontiers

Retinal Vessel Segmentation Algorithm Based on Residual Convolution Neural Network

Retinal vessels are the only deep micro vessels that can be observed in human body, the accurate identification of which has great significance on the diagnosis of hypertension, diabetes and other ...
IEEE

Two-Stream Graph Convolutional Network for Intra-Oral Scanner Image Segmentation

Abstract: Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often ...
Scientific Research Publishing

Food Ingredients Identification from Dish Images by Deep Learning ()

Diet plays an important role in people’s daily life with its strong correlation to health and chronic diseases. Meanwhile, deep based food computing emerges to provide lots of works which including ...
Nature

Abdominal multi-organ auto-segmentation using 3D-patch-based deep convolutional neural network

Segmentation of normal organs is a critical and time-consuming process in radiotherapy. Auto-segmentation of abdominal organs has been made possible by the advent of the convolutional neural network.
Frontiers

Using Deep Convolutional Neural Networks for Neonatal Brain Image Segmentation

Deep learning neural networks are especially potent at dealing with structured data, such as images and volumes. Both modified LiviaNET and HyperDense-Net performed well at a prior competition ...