A new study explores deep learning for image-based defect detection during 3D printing, looking to catch bad builds.

Background Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk ...

This is a tensorflow implementation of "Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network", a deep learning based Single-Image Super-Resolution (SISR) ...

Deep learning shows promising results in extracting useful information from medical images. The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract features that ...

This is an open source project from original of this: SRCNN_Cpp is a C++ Implementation of Image Super-Resolution using SRCNN which is proposed by Chao Dong in 2014. If you want to find the details of ...

Brain computer interaction (BCI) based on EEG can help patients with limb dyskinesia to carry out daily life and rehabilitation training. However, due to the low signal-to-noise ratio and large ...

Faces in real life convey categorical attributes (e.g., age), unique identities, and dynamic information (e.g., expression, attention). Deep convolutional neural networks (DCNNs) can be trained to ...

Five ILSVRC-2010 test images in the first column. Remaining columns show the training images that produce feature vectors in the last hidden layer with the smallest Euclidean distance from the feature ...