Neural networks : the official journal of the International Neural Network Society
Oct 5, 2024
Graph Neural Networks (GNNs) play a key role in efficiently learning node representations of graph-structured data through message passing, but their predictions are often correlated with sensitive attributes and thus lead to potential discrimination...
Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haemat...
Computer methods and programs in biomedicine
Oct 5, 2024
BACKGROUND AND OBJECTIVE: Fetal brain tissue segmentation provides foundational support for comprehensively understanding the neurodevelopment of normal and congenital disease-affected fetuses. Manual labeling is very time-consuming, and automated se...
International journal of medical informatics
Oct 5, 2024
BACKGROUND: Breast Cancer (BC) poses significant challenges in treatment decision-making. Multiple first treatment lines are currently available, determined by several patient-specific factors that need to be considered in the decision-making process...
Interdisciplinary sciences, computational life sciences
Oct 5, 2024
Cardiotocography (CTG) is used to assess the health of the fetus during birth or antenatally in the third trimester. It concurrently detects the maternal uterine contractions (UC) and fetal heart rate (FHR). Fetal distress, which may require therapeu...
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Oct 4, 2024
Cell misuse and cross-contamination pose a significant threat to the accuracy of cell research outcomes, often leading to the wasteful expenditure of time, manpower, and material resources. Consequently, the accurate identification of cell lines is p...
Neural networks : the official journal of the International Neural Network Society
Oct 4, 2024
Multimodal classification algorithms play an essential role in multimodal machine learning, aiming to categorize distinct data points by analyzing data characteristics from multiple modalities. Extensive research has been conducted on distilling mult...
OBJECTIVE: Training a neural network-based biomedical named entity recognition (BioNER) model usually requires extensive and costly human annotations. While several studies have employed multi-task learning with multiple BioNER datasets to reduce hum...
In the wake of growing concerns regarding diet-related health issues, this study investigates the application of machine learning methods to estimate the energy content and classify the health risks of foods based on the USDA National Nutrient Databa...
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