Latest AI and machine learning research in identifying and reporting child abuse for healthcare professionals.
The increasing expansion of biomedical documents has increased the number of natural language textual resources related to the current applications. Meanwhile, there has been a great interest in extracting useful information from meaningful coherent groupings of textual content documents in the last decade. However, it is challenging to discover informative representations and define relevant arti...
Recently, heatmap regression has been widely explored in facial landmark detection and obtained remarkable performance. However, most of the existing heatmap regression-based facial landmark detection methods neglect to explore the high-order feature correlations, which is very important to learn more representative features and enhance shape constraints. Moreover, no explicit global shape constra...
Existing inefficient traffic signal plans are causing traffic congestions in many urban areas. In recent years, many deep reinforcement learning (RL) ...
Typical image aesthetics assessment (IAA) is modeled for the generic aesthetics perceived by an "average" user. However, such generic aesthetics model...
BACKGROUND: MR-based methods for attenuation correction (AC) in PET/MRI either neglect attenuation of bone, or use MR-signal derived information about...
Morphological attributes from histopathological images and molecular profiles from genomic data are important information to drive diagnosis, prognosi...
Grounding natural language in images, such as localizing "the black dog on the left of the tree", is one of the core problems in artificial intelligen...
In this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis...
Despite Convolutional Neural Networks (CNNs) based approaches have been successful in objects detection, they predominantly focus on positioning discr...
In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human ac...
The COVID-19 pandemic continues to impact people worldwide-steadily depleting scarce resources in healthcare. Medical Artificial Intelligence (AI) pro...
Disease prediction is a well-known classification problem in medical applications. Graph Convolutional Networks (GCNs) provide a powerful tool for ana...
Social robots are increasingly penetrating our daily lives. They are used in various domains, such as healthcare, education, business, industry, and c...
The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two i...
Human motion prediction, which aims to predict future human poses given past poses, has recently seen increased interest. Many recent approaches are b...
Contemporary psycholinguistic models place significant emphasis on the cognitive processes involved in the acquisition, recognition, and production of...
Consumer groups are pressuring modern farmers to be more efficient with a focus on better animal welfare. Herding risks farmer lives, involves stress ...
Understanding how and where in the brain sentence-level meaning is constructed from words presents a major scientific challenge. Recent advances have ...
Person reidentification (Re-ID) aims to match observations of individuals across multiple nonoverlapping camera views. Recently, metric learning-based...