Accurate and precise brain tumor MR images classification plays important role in clinical
diagnosis and decision making for patient treatment. The key challenge in MR images …

Visual place recognition has attracted widespread research interest in multiple fields such
as computer vision and robotics. Recently, researchers have employed advanced deep …

We develop a novel deep learning framework to achieve highly accurate machine fault
diagnosis using transfer learning to enable and accelerate the training of deep neural …

This paper explores how well deep learning models trained on chest CT images can
diagnose COVID-19 infected people in a fast and automated process. To this end, we …

Image descriptors based on activations of Convolutional Neural Networks (CNNs) have
become dominant in image retrieval due to their discriminative power, compactness of …

Machine learning (ML) is increasingly being used in image retrieval systems for medical
decision making. One application of ML is to retrieve visually similar medical images from …

Diabetic retinopathy (DR) is a common complication of diabetes and one of the major
causes of blindness in the active population. Many of the complications of DR can be …

Training a deep convolutional neural network (CNN) from scratch is difficult because it
requires a large amount of labeled training data and a great deal of expertise to ensure …

In domain adaptation, maximum mean discrepancy (MMD) has been widely adopted as a
discrepancy metric between the distributions of source and target domains. However …

In recent years visual place recognition (VPR), ie, the problem of recognizing the location of
images, has received considerable attention from multiple research communities, spanning …