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   Articles with "limited data" as a keyword



An original deep learning model using limited data for COVID‐19 discrimination: A multicenter study

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recommendations! Published in 2022 at "Medical Physics"

DOI: 10.1002/mp.15549

Abstract: Abstract Objectives Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID‐19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of… read more here.

Keywords: covid discrimination; data covid; using limited; covid ... See more keywords

Patient-specific placental vessel segmentation with limited data

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recommendations! Published in 2024 at "Journal of Robotic Surgery"

DOI: 10.1007/s11701-024-01981-z

Abstract: A major obstacle in applying machine learning for medical fields is the disparity between the data distribution of the training images and the data encountered in clinics. This phenomenon can be explained by inconsistent acquisition… read more here.

Keywords: patient specific; segmentation; vessel segmentation; pipeline ... See more keywords

Learning from Few Samples with Memory Network

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recommendations! Published in 2017 at "Cognitive Computation"

DOI: 10.1007/s12559-017-9507-z

Abstract: Neural networks (NN) have achieved great successes in pattern recognition and machine learning. However, the success of a NN usually relies on the provision of a sufficiently large number of data samples as training data.… read more here.

Keywords: memory network; neural networks; learning samples; limited data ... See more keywords
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Developing Surrogate Markers for Predicting Antibiotic Resistance "Hot Spots" in Rivers Where Limited Data Are Available.

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recommendations! Published in 2021 at "Environmental science & technology"

DOI: 10.1021/acs.est.1c00939

Abstract: Pinpointing environmental antibiotic resistance (AR) hot spots in low-and middle-income countries (LMICs) is hindered by a lack of available and comparable AR monitoring data relevant to such settings. Addressing this problem, we performed a comprehensive… read more here.

Keywords: resistance hot; water quality; antibiotic resistance; limited data ... See more keywords

Super-Resolution Residual U-Net Model for the Reconstruction of Limited-Data Tunable Diode Laser Absorption Tomography

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recommendations! Published in 2022 at "ACS Omega"

DOI: 10.1021/acsomega.2c01435

Abstract: Resolution is an important index for evaluating the reconstruction performance of temperature distributions in a combustion environment, and a higher resolution is necessary to obtain more precise combustion diagnoses. Tunable diode laser absorption tomography (TDLAT)… read more here.

Keywords: tunable diode; super resolution; resolution; reconstruction ... See more keywords

Weighted Bayesian uncertainty quantification for the high explosive reactants using limited data

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recommendations! Published in 2025 at "AIP Advances"

DOI: 10.1063/5.0244326

Abstract: Bayesian uncertainty analysis is a highly effective tool for estimating model uncertainty, thereby improving the prediction ability with limited data. The data quality plays a role in uncertainty analysis. This paper presents a novel approach… read more here.

Keywords: quantification; limited data; uncertainty quantification; uncertainty ... See more keywords

Probabilistic back analysis for landslide susceptibility assessment in data-scarce regions using a Bayesian approach

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recommendations! Published in 2024 at "Geomatics, Natural Hazards and Risk"

DOI: 10.1080/19475705.2024.2434616

Abstract: Abstract Incomplete knowledge about geotechnical conditions, owing to limited data availability, leads to uncertainties in slope stability analysis. Therefore, probabilistic back analysis has been adopted to obtain additional information and control the uncertainty, but the… read more here.

Keywords: landslide susceptibility; analysis; probabilistic back; limited data ... See more keywords

A PINN-driven game-theoretic framework in limited data photoacoustic tomography

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recommendations! Published in 2025 at "Inverse Problems"

DOI: 10.1088/1361-6420/ae1bcd

Abstract: This paper presents a novel methodological framework to obtain superior reconstructions in limited data photoacoustic tomography. The proposed framework exploits the presence of Cauchy data on an accessible part of the observation domain and uses… read more here.

Keywords: data photoacoustic; framework; photoacoustic tomography; limited data ... See more keywords

Parameter selection in limited data cone-beam CT reconstruction using edge-preserving total variation algorithms.

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recommendations! Published in 2017 at "Physics in medicine and biology"

DOI: 10.1088/1361-6560/aa93d3

Abstract: There are a number of powerful total variation (TV) regularization methods that have great promise in limited data cone-beam CT reconstruction with an enhancement of image quality. These promising TV methods require careful selection of… read more here.

Keywords: reconstruction; selection; total variation; data cone ... See more keywords
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An Overview of Parametric Modeling and Methods for Radar Target Detection With Limited Data

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recommendations! Published in 2021 at "IEEE Access"

DOI: 10.1109/access.2021.3074063

Abstract: This article provides a survey of recent results on exploiting parametric auto-regressive (AR) models for adaptive radar target detection. Specifically, three types of radar systems are considered, including phased-array radar with multiple co-located transmitters and… read more here.

Keywords: limited data; radar; target detection; radar target ... See more keywords

A Coarse-to-Fine Hierarchical Feature Learning for SAR Automatic Target Recognition With Limited Data

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recommendations! Published in 2024 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"

DOI: 10.1109/jstars.2024.3423377

Abstract: With the rapid advancements in deep learning, Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) has seen significant improvements in performance. However, the effectiveness of even the most advanced deep-learning-based ATR methods is limited by… read more here.

Keywords: recognition; coarse fine; limited data; feature ... See more keywords