Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …

Ongoing improvements in AI, particularly concerning deep learning techniques, are
assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the …

Integrating artificial intelligence with food category recognition has been a field of interest for
research for the past few decades. It is potentially one of the next steps in revolutionizing …

Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …

In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …

Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …

Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …

Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

Registration for multisensor or multimodal image pairs with a large degree of distortions is a
fundamental task for many remote sensing applications. To achieve accurate and low-cost …