IEEE journal of biomedical and health informatics
Jun 5, 2023
The increasing prevalence of chronic non-communicable diseases makes it a priority to develop tools for enhancing their management. On this matter, Artificial Intelligence algorithms have proven to be successful in early diagnosis, prediction and ana...
Weakly supervised segmentation (WSS) aims to exploit weak forms of annotations to achieve the segmentation training, thereby reducing the burden on annotation. However, existing methods rely on large-scale centralized datasets, which are difficult to...
This paper presents an innovative methodology to study the application of seasonality (the existence of cyclical patterns) to help predict the level of crime. This methodology combines the simplicity of entropy-based metrics that describe temporal pa...
Despite its potentials benefits, using prediction targets generated based on latent variable (LV) modeling is not a common practice in supervised learning, a dominating framework for developing prediction models. In supervised learning, it is typical...
In the sea of data generated daily, unlabeled samples greatly outnumber labeled ones. This is due to the fact that, in many application areas, labels are scarce or hard to obtain. In addition, unlabeled samples might belong to new classes that are no...
The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fully-supervised approaches for training such models require vast amount...
International journal of pharmaceutics
May 20, 2023
The purpose of this study is to demonstrate the usefulness of machine learning (ML) for analyzing a material attribute database from tablets produced at different granulation scales. High shear wet granulators (scale 30 g and 1000 g) were used and da...
Neural networks : the official journal of the International Neural Network Society
May 19, 2023
Recent years have witnessed an increasing focus on graph-based semi-supervised learning with Graph Neural Networks (GNNs). Despite existing GNNs having achieved remarkable accuracy, research on the quality of graph supervision information has inadver...
Deep learning technology has been used in the medical field to produce devices for clinical practice. Deep learning methods in cytology offer the potential to enhance cancer screening while also providing quantitative, objective, and highly reproduci...
PURPOSE: Classification and grading of central nervous system (CNS) tumours play a critical role in the clinic. When WHO CNS5 simplifies the histopathology diagnosis and places greater emphasis on molecular pathology, artificial intelligence (AI) has...