AIMC Topic: Data Accuracy

Clear Filters Showing 91 to 100 of 187 articles

Developing a novel force forecasting technique for early prediction of critical events in robotics.

PloS one
Safety critical events in robotic applications can often be characterized by forces between the robot end-effector and the environment. One application in which safe interaction between the robot and environment is critical is in the area of medical ...

Assessing alternative methods for unsupervised segmentation of urban vegetation in very high-resolution multispectral aerial imagery.

PloS one
To analyze types and patterns of greening trends across a city, this study seeks to identify a method of creating very high-resolution urban vegetation maps that scales over space and time. Vegetation poses unique challenges for image segmentation be...

Big Data and Atrial Fibrillation: Current Understanding and New Opportunities.

Journal of cardiovascular translational research
Atrial fibrillation (AF) is the most common arrhythmia with diverse etiology that remarkably relates to high morbidity and mortality. With the advancements in intensive clinical and basic research, the understanding of electrophysiological and pathop...

Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer.

Scientific reports
A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data lea...

Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks.

PloS one
Cytology is the first pathological examination performed in the diagnosis of lung cancer. In our previous study, we introduced a deep convolutional neural network (DCNN) to automatically classify cytological images as images with benign or malignant ...

Features spaces and a learning system for structural-temporal data, and their application on a use case of real-time communication network validation data.

PloS one
The service quality and system dependability of real-time communication networks strongly depends on the analysis of monitored data, to identify concrete problems and their causes. Many of these can be described by either their structural or temporal...

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis.

European radiology
OBJECTIVES: To propose a transfer learning (TL) radiomics model that efficiently combines the information from gray scale and elastogram ultrasound images for accurate liver fibrosis grading.