Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and therefore time- and cost-intensive. Any imaging artifacts originating fr…
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48 results for “Image Quality Assessment”
Quality Assessment of Low Light Restored Images: A Subjective Study and an Unsupervised Modeleess.IV
Study subjective perception of low light restored images and develop an unsupervised QA model.
problem Lack of subjective QA for low light restored images and challenges in collecting human opinion scores.
method Create a dataset, conduct subjective QA study, develop self-supervised contrastive learning technique to extract features.
result Unsupervised NR QA model achieves state-of-the-art performance for low light restored images.
Paper proposes a method to protect DCNNs from adversarial attacks and poor quality images.
problem DCNNs are vulnerable to adversarial examples and perceptual quality issues.
method Combines image quality assessment with knowledge distillation.
result Detector effectively identifies poor quality and adversarial images.
Paper proposes a method to estimate intra-observer variability in echocardiography quality assessment.
problem Intra-observer variability in echocardiography quality assessment impacts deep neural network reliability.
method Modeling intra-observer variability as aleatoric uncertainty in a regression problem.
result The proposed method reduces error from 0.11 to 0.09, improving test accuracy by 5.7%.
The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should comprise quality monitoring of the input images, segmentation of the …
Proposes a new PCA method using SSIM for image subspace learning.
problem Lack of principled assessment methods for image fidelity and similarity.
method Defines an image structure subspace using SSIM, proposes ISCA and kernel ISCA.
result Demonstrates improved image subspace learning using SSIM.
Adapts deep learning models trained on simulated images for use with real images.
problem Difficulty in training deep neural networks on large amounts of experimental data.
method Adversarial domain adaptation method to mitigate domain shift between simulated and experimental image data.
result Adversarial domain adaptation successfully mitigates domain shift and improves numerical observer performance.
CatSIM measures image similarity robustly to small changes.
problem Measuring similarity between images, especially with small perturbations.
method Uses structural similarity image quality paradigm, robust to small location changes.
result Structural similarity between images rated higher when not entirely overlapping.
A method uses CG to create efficient channels for ideal observers.
problem Computational intractability of ideal observers for high-dimensional image data.
method Conjugate gradient (CG) method for constructing efficient channels.
result CG-based channels approximate IO and HO performance efficiently.
Automates quality control for synthetic CTs generated from MR images.
problem Prevent downstream errors in RT treatment planning from synthetic CTs.
method Ensemble of sCT generators and uncertainty measure based on their disagreement.
result Uncertainty measure can detect input images outside expected MR distribution and sCT images with potential errors.
The impact of MRI image quality on statistical and predictive analysis on voxel based morphologyeess.IV
MRI image quality affects statistical and predictive analysis of brain morphology.
problem Impact of MRI image quality on statistical and predictive analysis of brain morphology.
method Systematic testing of image quality on univariate statistics and machine learning classification using three large datasets.
result Low-quality MRI data significantly affects detecting significant sex/gender differences in smaller samples, but not in larger ones.
Generative models assess quality on time-series data using ITS and FITD.
problem Lack of consensus for quality assessment of class-conditional generative models on time-series data.
method Introduced InceptionTime Score (ITS) and Frechet InceptionTime Distance (FITD) to evaluate generative models.
result ITS and FITD combined with TSTR can accurately assess generative model performance on time-series data.
Paper introduces LLISE for image structure learning using SSIM.
problem Image quality assessment using MSE or norm is not promising.
method Locally Linear Image Structural Embedding (LLISE) using SSIM.
result LLISE captures image structure features and discriminates distortions.
Neural network estimates rigid motion in stroke imaging to improve image quality.
problem Rigid patient motion during C-arm CBCT imaging reduces image quality.
method Neural network trained to regress reprojection error based on image information.
result Neural network outperforms entropy-based method in motion estimation.
OTRE uses OT to improve retinal images, outperforming existing methods.
problem Improving quality of non-mydriatic retinal images for accurate diagnoses.
method OT theory for image-to-image translation, regularization by enhancing.
result OTRE outperforms state-of-the-art methods on various retinal image tasks.
Study evaluates deep learning methods for dermatology, finding they perform poorly under non-ideal conditions.
problem Lack of robustness of deep learning methods in dermatology under real-world conditions.
method Simulated non-ideal conditions on user-submitted dermatology images.
result Deep learning methods show significant drop in accuracy and prediction changes under non-ideal conditions.
The paper evaluates and improves uncertainty estimates in neural networks for safety-critical applications.
problem Quantifying uncertainty in neural networks for safety-critical systems.
method Proposes a statistical test for evaluating uncertainty realism in neural networks and transfers a classification architecture to image-to-image tasks.
result The variational U-Net architecture significantly improves uncertainty realism in image-to-image tasks compared to a plain model.
Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 r…
Researchers develop tests to assess quality of GAN-generated images.
problem Lack of objective means to evaluate domain-relevant quality of GAN-generated images.
method Designed stochastic context models (SCMs) and statistical classifiers to detect high-order spatial arrangements in GAN-generated images.
result GANs can generate images that appear accurate visually but lack specific high-order spatial arrangements.
Protein structure prediction has been a grand challenge problem in the structure biology over the last few decades. Protein quality assessment plays a very important role in protein structure prediction. In the paper, we propose a new protein quality assessment method which can predict both local and global quality of …
Improves image quality in generative models by estimating pixel-wise aleatoric uncertainty.
problem Lack of quantitative assessment of image quality in diffusion models.
method Estimate pixel-wise aleatoric uncertainty during sampling phase using a perturbation scheme designed for diffusion models.
result Uncertainty-guided sampling leads to better sample generation quality as shown by FID scores.
With the advent of smartphone indirect ophthalmoscopy, teleophthalmology - the use of specialist ophthalmology assets at a distance from the patient - has experienced a breakthrough, promising enormous benefits especially for healthcare in distant, inaccessible or opthalmologically underserved areas, where specialists …
Super-resolution methods form high-resolution images from low-resolution images. In this paper, we develop a new Bayesian nonparametric model for super-resolution. Our method uses a beta-Bernoulli process to learn a set of recurring visual patterns, called dictionary elements, from the data. Because it is nonparametric…
The paper assesses quality measures for machine learning models using cross-validation.
problem Evaluating the accuracy and robustness of quality measures for machine learning models.
method Cross-validation approach to estimate prediction error and quantify explained variation. Confidence bounds and local quality measures derived from residuals.
result The reliability and robustness of quality measures are assessed through numerical examples and confidence bounds.
Automatically assesses the quality of online health articles.
problem Lack of automated tools to evaluate the quality of online health information.
method Data mining approach using 10 quality criteria and feature selection.
result Classifier achieved 84%-90% accuracy on 10 criteria.
Study improves data quality assessment for structural monitoring data.
problem Ensuring reliability of structural health monitoring data.
method Probabilistic data quality assessment using a conditional diffusion model.
result Significantly improves accuracy of data quality assessment.
An image-driven approach for time series forecasting.
problem Time-series forecasting as a computer vision task.
method Capture input data as an image and train a model to produce the subsequent image.
result Our method outperforms various baselines, including ARIMA, using image-based evaluation metrics.
New methods for functional data analysis improve manifold methods for continuous data.
problem Challenges in evaluating embeddings for functional data.
method Transfer manifold methods from tabular and image data to functional data, define a theoretical framework, and propose nuanced evaluation strategies.
result Manifold methods can be successfully applied to functional data, but careful evaluation is needed.
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial contexteess.IV
DDPMs can reproduce medical image context, showing interpolation between samples.
problem Understanding DDPMs' ability to learn spatial context in medical imaging.
method Used stochastic context models (SCMs) to produce training data and assess DDPMs' performance.
result DDPMs can generate contextually correct images, interpolating between samples.
A framework assesses the quality of crowdsourced weather data.
problem Quality control and assessment of crowdsourced weather data from third-party stations.
method Proposes a simple, scalable, and interpretable AI/Stats/ML framework to assess TPAWS data.
result Demonstrates the performance of the framework using synthetic and real data.
There exists a vast number of adversarial attacks and defences for machine learning algorithms of various types which makes assessing the robustness of algorithms a daunting task. To make matters worse, there is an intrinsic bias in these adversarial algorithms. Here, we organise the problems faced: a) Model Dependence…
Over the past decade, many Super Resolution techniques have been developed using deep learning. Among those, generative adversarial networks (GAN) and very deep convolutional networks (VDSR) have shown promising results in terms of HR image quality and computational speed. In this paper, we propose two approaches based…
Generative Adversarial Networks (GANs) are an elegant mechanism for data generation. However, a key challenge when using GANs is how to best measure their ability to generate realistic data. In this paper, we demonstrate that an intrinsic dimensional characterization of the data space learned by a GAN model leads to an…
The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving. We here provide an easy-to-use benchmark to assess how object detection models perform when image quality degrades. The three resulting benchmark datasets,…
This paper uses diffusion models for lossy image compression, improving perceptual metrics and practicality.
problem Lossy image compression with improved perceptual metrics and practicality.
method End-to-end optimized lossy image compression using conditional diffusion models.
result The model yields stronger FID scores and competitive performance in distortion metrics.
This paper presents two approaches for filter design based on stochastic distances for intensity speckle reduction. A window is defined around each pixel, overlapping samples are compared and only those which pass a goodness-of-fit test are used to compute the filtered value. The tests stem from stochastic divergences …
Convolutional Neural Networks are a well-known staple of modern image classification. However, it can be difficult to assess the quality and robustness of such models. Deep models are known to perform well on a given training and estimation set, but can easily be fooled by data that is specifically generated for the pu…
Evaluates deep learning models in histopathology for robustness and classification strategies.
problem Lack of comprehensive evaluation of histopathology models beyond accuracy.
method Developed a new methodology to evaluate models on five histopathology datasets, including vision transformers and CNNs.
result Identified insights into cancer classification strategies and robustness against stain variations.
Deep learning segments spinal metastases in MR images.
problem Challenges in accurately segmenting spinal metastases in MR images.
method Used a U-Net-like architecture trained on 40 clinical cases.
result Average Dice scores up to 77.6% and mean sensitivity rates up to 78.9%.
In this paper, an ensemble-based method for the screening of diabetic retinopathy (DR) is proposed. This approach is based on features extracted from the output of several retinal image processing algorithms, such as image-level (quality assessment, pre-screening, AM/FM), lesion-specific (microaneurysms, exudates) and …
Histopathological images of tumors contain abundant information about how tumors grow and how they interact with their micro-environment. Better understanding of tissue phenotypes in these images could reveal novel determinants of pathological processes underlying cancer, and in turn improve diagnosis and treatment opt…
This paper presents a new approach for filter design based on stochastic distances and tests between distributions. A window is defined around each pixel, overlapping samples are compared and only those which pass a goodness-of-fit test are used to compute the filtered value. The technique is applied to intensity SAR d…
The paper defines and assesses the quality of datasets using a novel expected diameter metric.
problem Lack of rigorous methods to assess data quality.
method Formal definition of data quality, expected diameter metric, Fourier analysis, algebraic methods, probabilistic analysis.
result The expected diameter metric provides theoretical guarantees and practical solutions for data quality assessment.
The UN Sustainable Development Goals allude to the importance of infrastructure quality in three of its seventeen goals. However, monitoring infrastructure quality in developing regions remains prohibitively expensive and impedes efforts to measure progress toward these goals. To this end, we investigate the use of wid…
Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. Acquiring more relevant infor…
Framework for automatically assessing and correcting data quality issues without domain knowledge.
problem Ensuring data quality in datasets across various domains.
method Hybrid approach combining statistical and machine learning methods.
result Effective detection and correction of missing values, duplicates, and typographical errors.
The paper introduces a method to assess machine translation quality with confidence intervals.
problem Evaluating the uncertainty and quality of machine translation.
method Utilizes conformal predictive distributions to produce prediction intervals with guaranteed coverage.
result The method outperforms a baseline on six language pairs in terms of coverage and sharpness.
PQMass assesses generative model quality using chi-squared tests.
problem Assessing the quality of generative models without density assumptions.
method Divides sample space into regions, applies chi-squared tests to p-values.
result Effectively assesses generative model quality, novelty, and diversity.