Latest AI and machine learning research in lymphoma for healthcare professionals.
Non-preference-based patient-reported outcome measures (PROMs) are popular in health outcomes resear...
We investigate the use of 3D convolutional neural networks for gamma arrival time estimation in mono...
BACKGROUND: To determine the maturity of cantaloupe, measuring the soluble solid content (SSC) as th...
Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-...
INTRODUCTION: Incisional hernias can complicate up to 25% of laparotomies, and successful repair rem...
OBJECTIVES: To evaluate the value of deep learning (DL) combining multimodal radiomics and clinical ...
Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants ...
In water resources management, modeling water balance factors is necessary to control dams, agricult...
Cardiovascular diseases (CVD) are the leading cause of death worldwide. People affected by CVDs may ...
BACKGROUND: The non-local module has been primarily used in literature to capturing long-range depen...
Graph Neural Networks (GNNs) have been widely applied to various fields due to their powerful repres...
Since preparative chromatography is a sustainability challenge due to large amounts of consumables u...
Quantification of uncertainty in deep-neural-networks (DNN) based image registration algorithms play...
The task to develop a mechanism for predicting the hemodynamic parameters values based on non-invasi...
T cell acute lymphoblastic leukemia (T-ALL) is invasive and heterogeneous, and existing therapies ar...
The demand for cost-efficient manufacturing of complex metal components has driven research for meta...
Malaria diagnosis based on microscopy is impaired by the gradual disappearance of experienced micros...
This paper presents a novel design and development of a low-cost and multi-touch sensor based on cap...
The traditional risk management and control mode (RMCM) in regional sites has the defects of low eff...
Large neural networks usually perform well for executing machine learning tasks. However, models tha...
This retrospective study aimed to develop and validate a deep learning model for the classification ...