AIMC Topic: Neural Networks, Computer

Clear Filters Showing 7261 to 7270 of 31376 articles

Registered multi-device/staining histology image dataset for domain-agnostic machine learning models.

Scientific data
Variations in color and texture of histopathology images are caused by differences in staining conditions and imaging devices between hospitals. These biases decrease the robustness of machine learning models exposed to out-of-domain data. To address...

Evaluation of monolithic crystal detector with dual-ended readout utilizing multiplexing method.

Physics in medicine and biology
Monolithic crystal detectors are increasingly being applied in positron emission tomography (PET) devices owing to their excellent depth-of-interaction (DOI) resolution capabilities and high detection efficiency. In this study, we constructed and eva...

SC-SSL: Self-Correcting Collaborative and Contrastive Co-Training Model for Semi-Supervised Medical Image Segmentation.

IEEE transactions on medical imaging
Image segmentation achieves significant improvements with deep neural networks at the premise of a large scale of labeled training data, which is laborious to assure in medical image tasks. Recently, semi-supervised learning (SSL) has shown great pot...

Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys.

PloS one
Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine...

LMGATCDA: Graph Neural Network With Labeling Trick for Predicting circRNA-Disease Associations.

IEEE/ACM transactions on computational biology and bioinformatics
Previous studies have proven that circular RNAs (circRNAs) are inextricably connected to the etiology and pathophysiology of complicated diseases. Since conventional biological research are frequently small-scale, expensive, and time-consuming, it is...

Learning From an Artificial Neural Network in Phylogenetics.

IEEE/ACM transactions on computational biology and bioinformatics
We show that an iterative ansatz of deep learning and human intelligence guided simplification may lead to surprisingly simple solutions for a difficult problem in phylogenetics. Distinguishing Farris and Felsenstein trees is a longstanding problem i...

Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB - retrained AlexNet convolutional neural network.

F1000Research
BACKGROUND: Glaucoma and diabetic retinopathy (DR) are the leading causes of irreversible retinal damage leading to blindness. Early detection of these diseases through regular screening is especially important to prevent progression. Retinal fundus ...

A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System.

Journal of imaging informatics in medicine
This study aims to provide an effective solution for the autonomous identification of dental implant brands through a deep learning-based computer diagnostic system. It also seeks to ascertain the system's potential in clinical practices and to offer...

Comprehensive data analysis of white blood cells with classification and segmentation by using deep learning approaches.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Deep learning approaches have frequently been used in the classification and segmentation of human peripheral blood cells. The common feature of previous studies was that they used more than one dataset, but used them separately. No study has been fo...

SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. The accurate survival prediction for CRC patients plays a significant role in the formulation of treatment strategies. Recently, machine learni...