AIMC Topic: Neural Networks, Computer

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Deep learning algorithm for predicting preterm birth in the case of threatened preterm labor admissions using transvaginal ultrasound.

Journal of medical ultrasonics (2001)
PURPOSE: Preterm birth presents a major challenge in perinatal care, and predicting preterm birth remains a major challenge. If preterm birth cases can be accurately predicted during pregnancy, preventive interventions and more intensive prenatal mon...

Evaluation of mediastinal lymph node segmentation of heterogeneous CT data with full and weak supervision.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate lymph node size estimation is critical for staging cancer patients, initial therapeutic management, and assessing response to therapy. Current standard practice for quantifying lymph node size is based on a variety of criteria that use uni-d...

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments.

Journal of visualized experiments : JoVE
Salient object detection has emerged as a burgeoning area of interest within the realm of computer vision. However, prevailing algorithms exhibit diminished precision when tasked with detecting salient objects within intricate and multifaceted enviro...

Cellstitch: 3D cellular anisotropic image segmentation via optimal transport.

BMC bioinformatics
BACKGROUND: Spatial mapping of transcriptional states provides valuable biological insights into cellular functions and interactions in the context of the tissue. Accurate 3D cell segmentation is a critical step in the analysis of this data towards u...

Insight into Automatic Image Diagnosis of Ear Conditions Based on Optimized Deep Learning Approach.

Annals of biomedical engineering
Examining otoscopic images for ear diseases is necessary when the clinical diagnosis of ear diseases extracted from the knowledge of otolaryngologists is limited. Improved diagnosis approaches based on otoscopic image processing are urgently needed. ...

Meta-structure-based graph attention networks.

Neural networks : the official journal of the International Neural Network Society
Due to the ubiquity of graph-structured data, Graph Neural Network (GNN) have been widely used in different tasks and domains and good results have been achieved in tasks such as node classification and link prediction. However, there are still many ...

Training multi-source domain adaptation network by mutual information estimation and minimization.

Neural networks : the official journal of the International Neural Network Society
We address the problem of Multi-Source Domain Adaptation (MSDA), which trains a neural network using multiple labeled source datasets and an unlabeled target dataset, and expects the trained network to well classify the unlabeled target data. The mai...

Real-time water quality prediction in water distribution networks using graph neural networks with sparse monitoring data.

Water research
Ensuring the safety and reliability of drinking water supply requires accurate prediction of water quality in water distribution networks (WDNs). However, existing hydraulic model-based approaches for system state prediction face challenges in model ...

Deep learning for report generation on chest X-ray images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Medical imaging, specifically chest X-ray image analysis, is a crucial component of early disease detection and screening in healthcare. Deep learning techniques, such as convolutional neural networks (CNNs), have emerged as powerful tools for comput...

Toxicity prediction of nanoparticles using machine learning approaches.

Toxicology
Nanoparticle toxicity analysis is critical for evaluating the safety of nanomaterials due to their potential harm to the biological system. However, traditional experimental methods for evaluating nanoparticle toxicity are expensive and time-consumin...