Statistical framework for validation without ground truth of choroidal thickness changes detection

The objective of this work was to relocate the entire seismicity of the Pannonian Basin with the Bayesloc algorithm, a Markov-Chain Monte Carlo inversion scheme using a Bayesian statistical framework.In the Hungarian National Seismological Bulletin the magnitudes and event locations are determined with the iLoc location algorithm using the 3D global RSTT velocity model, and we used these locations as initial coordinates. In our work, we have used all of the instrumentally registered seismic events between 1996 and 2019 in the Pannonian Basin.During data preprocessing we used graph theory to measure data connectivity. Similar to all multiple-event location methods, Bayesloc performs better when events are recorded on a common network. We used several hundreds of ground truth events (quarry blasts, mine explosions, earthquakes) to tie down the seismicity pattern to known ground truth locations by giving them tighter prior distributions.Based on the day-time peak on the origin-hour distribution of the bulletin earthquakes we assume that there are anthropogenic events labeled as earthquakes in the catalog, therefore we created a &#8222;Suspected explosions (SX)&#8221; group to set prior constrains.The results show that the events around the mines are dramatically better clustered. The prior constraints contributed remarkably to the outcome of the relocation. We show that the results present an improved view of the seismicity of the region.

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Challenges in Peptide-Spectrum Matching: a Robust and Reproducible Statistical Framework for Removing Low-Accuracy, High-Scoring Hits

10.1101/839290 ◽

2019 ◽

Author(s):

Shane L. Hubler ◽

Praveen Kumar ◽

Subina Mehta ◽

Caleb Easterly ◽

James E. Johnson ◽

...

Keyword(s):

Large Scale ◽

Method Development ◽

Ground Truth ◽

Shotgun Proteomics ◽

Confidence Score ◽

Reference Database ◽

Confidence Measure ◽

Statistical Framework ◽

Single Organism ◽

Spectrum Matching

AbstractWorkflows for large-scale (MS)-based shotgun proteomics can potentially lead to costly errors in the form of incorrect peptide spectrum matches (PSMs). To improve robustness of these workflows, we have investigated the use of the precursor mass discrepancy (PMD) to detect and filter potentially false PSMs that have, nonetheless, a high confidence score. We identified and addressed three cases of unexpected bias in PMD results: time of acquisition within a LC-MS run, decoy PSMs, and length of peptide. We created a post-analysis Bayesian confidence measure based on score and PMD, called PMD-FDR. We tested PMD-FDR on four datasets across three types of MS-based proteomics projects: standard (single organism; reference database), proteogenomics (single organism; customized genomic-based database plus reference), and metaproteomics (microorganism community; customized conglomerate database). On a ground truth dataset and other representative data, PMD-FDR was able to detect 60-80% of likely incorrect PSMs (false-hits) while losing only 5% of correct PSMs (true-hits). PMD-FDR can also be used to evaluate data quality for results generated within different experimental PSM-generating workflows, assisting in method development. Going forward, PMD-FDR should provide detection of high-scoring but likely false-hits, aiding applications which rely heavily on accurate PSMs, such as proteogenomics and metaproteomics.

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Choroidal thickness maps from spectral domain and swept source optical coherence tomography: algorithmic versus ground truth annotation

British Journal of Ophthalmology ◽

10.1136/bjophthalmol-2015-307985 ◽

2016 ◽

Vol 100 (10) ◽

pp. 1372-1376 ◽

Cited By ~ 22

Author(s):

Ana-Maria Philip ◽

Bianca S Gerendas ◽

Li Zhang ◽

Henrik Faatz ◽

Dominika Podkowinski ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Choroidal Thickness ◽

Ground Truth ◽

Spectral Domain ◽

Optical Coherence ◽

Swept Source

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Forest and Industrial Plantation Mapping with Multi-Temporal Dual Polarization by ALOS PALSAR Data In Riau Province

International Journal of Environment and Geosciences ◽

10.24843/ijeg.2018.v02.i01.p04 ◽

2018 ◽

Vol 2 (1) ◽

pp. 35

Author(s):

Elis Molidena ◽

Takahiro Osawa ◽

Putu Gede Ardhana ◽

Abd. Rahman As-syakur

Keyword(s):

Land Use ◽

Ground Truth ◽

Temporal Analysis ◽

Alos Palsar ◽

Ground Truth Data ◽

Multi Temporal ◽

Industrial Plantation ◽

Changes Detection ◽

Accuracy Result ◽

Color Composite

Backscattering characteristics of land use has been analyzed using ALOS PALSAR data. The purpose of this research are mapping of land use by five categories such as forest, acacia, oil palm, open area and water, and to identify the changes of environmental. Analysis Pixel-by-pixel average of ALOS PALSAR level 1.5 backscattering used from five of category land use was to estimate the spectral characteristic of each object in difference HH and HV polarization. Ground truth data was taken from 169 locations which used for classification, 119 locations and 50 locations used for validation. Two different times of ALOS PALSAR level 1.0 2009 and 2010 data, was used for changes detection by multi temporal color composite combination. The accuracy result for classification map shows 62% of ground truth database, and multi temporal analysis showed the possibility of changes.

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LAND COVER CHANGES DETECTION IN POLARIMETRIC SAR DATA USING ALGEBRA, SIMILARITY AND DISTANCE BASED METHODS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w4-195-2017 ◽

2017 ◽

Vol XLII-4/W4 ◽

pp. 195-200 ◽

Cited By ~ 1

Author(s):

A. Najafi ◽

M. Hasanlou ◽

V. Akbari

Keyword(s):

Electromagnetic Waves ◽

Ground Truth ◽

False Alarms ◽

Polarimetric Sar ◽

Ground Truth Data ◽

Image Differencing ◽

Radar Images ◽

Area Under Roc Curve ◽

Changes Detection ◽

Auc Value

Monitoring and surveillance changes around the world need powerful methods, so detection, visualization, and assessment of significant changes are essential for planning and management. Incorporating polarimetric SAR images due to interactions between electromagnetic waves and target and because of the high spatial resolution almost one meter can be used to study changes in the Earth's surface. Full polarized radar images comparing to single polarized radar images use amplitude and phase information of the surface in different available polarization (HH, HV, VH, and VV). This study is based on the decomposition of full polarized airborne UAVSAR images and integration of these features with algebra method involves Image Differencing (ID) and Image Ratio (IR) algorithms with the mathematical nature and distance-based method involves Canberra (CA) and Euclidean (ED) algorithms with measuring distance between corresponding vector and similarity-based method involves Taminoto (TA) and Kulczynski (KU) algorithms with dependence corresponding vector for change detecting purposes on two real PolSAR datasets. Assessment of incorporated methods is implemented using ground truth data and different criteria for evaluating such as overall accuracy (OA), area under ROC curve (AUC) and false alarms rate (FAR). The output results show that ID, IR, and CA have superiority to detect changes comparing to other implemented algorithms. Also, numerical results show that the highest performance in two datasets has OA more than 90%. In other assessment criteria, mention algorithms have low FAR and high AUC value indices to detect changes in PolSAR images.

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A Hierarchy of Multinomial Models for Multidimensional Source Monitoring

Methodology ◽

10.1027/1614-1881.1.1.2 ◽

2005 ◽

Vol 1 (1) ◽

pp. 2-17 ◽

Cited By ~ 21

Author(s):

Thorsten Meiser

Keyword(s):

Cognitive Processes ◽

Source Monitoring ◽

Source Information ◽

Model Comparisons ◽

Storage And Retrieval ◽

Multinomial Processing Tree ◽

Multinomial Models ◽

Hierarchical Relationship ◽

Statistical Framework ◽

Tree Models

Abstract. Several models have been proposed for the measurement of cognitive processes in source monitoring. They are specified within the statistical framework of multinomial processing tree models and differ in their assumptions on the storage and retrieval of multidimensional source information. In the present article, a hierarchical relationship is demonstrated between multinomial models for crossed source information ( Meiser & Bröder, 2002 ), for partial source memory ( Dodson, Holland, & Shimamura, 1998 ) and for several sources ( Batchelder, Hu, & Riefer, 1994 ). The hierarchical relationship allows model comparisons and facilitates the specification of identifiability conditions. Conditions for global identifiability are discussed, and model comparisons are illustrated by reanalyses and by a new experiment on the storage and retrieval of multidimensional source information.

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A Comparison of Simple Structure Rotation Criteria in Temporal Exploratory Factor Analysis for Event-Related Potential Data

Methodology ◽

10.1027/1614-2241/a000175 ◽

2019 ◽

Vol 15 (Supplement 1) ◽

pp. 43-60 ◽

Cited By ~ 3

Author(s):

Florian Scharf ◽

Steffen Nestler

Keyword(s):

Factor Analysis ◽

Exploratory Factor Analysis ◽

Simple Structure ◽

Ground Truth ◽

Event Related Potential ◽

Suitable Alternative ◽

Factor Rotation ◽

Rotation Methods ◽

Simple Structure Rotation ◽

Temporal Overlap

Abstract. It is challenging to apply exploratory factor analysis (EFA) to event-related potential (ERP) data because such data are characterized by substantial temporal overlap (i.e., large cross-loadings) between the factors, and, because researchers are typically interested in the results of subsequent analyses (e.g., experimental condition effects on the level of the factor scores). In this context, relatively small deviations in the estimated factor solution from the unknown ground truth may result in substantially biased estimates of condition effects (rotation bias). Thus, in order to apply EFA to ERP data researchers need rotation methods that are able to both recover perfect simple structure where it exists and to tolerate substantial cross-loadings between the factors where appropriate. We had two aims in the present paper. First, to extend previous research, we wanted to better understand the behavior of the rotation bias for typical ERP data. To this end, we compared the performance of a variety of factor rotation methods under conditions of varying amounts of temporal overlap between the factors. Second, we wanted to investigate whether the recently proposed component loss rotation is better able to decrease the bias than traditional simple structure rotation. The results showed that no single rotation method was generally superior across all conditions. Component loss rotation showed the best all-round performance across the investigated conditions. We conclude that Component loss rotation is a suitable alternative to simple structure rotation. We discuss this result in the light of recently proposed sparse factor analysis approaches.

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Brain FDG PET for the Etiological Diagnosis of Clinically Uncertain Cognitive Impairment During Delirium in Remission

Journal of Alzheimer s Disease ◽

10.3233/jad-200530 ◽

2020 ◽

Vol 77 (4) ◽

pp. 1609-1622

Author(s):

Franziska Mathies ◽

Catharina Lange ◽

Anja Mäurer ◽

Ivayla Apostolova ◽

Susanne Klutmann ◽

...

Keyword(s):

Cognitive Impairment ◽

Fdg Pet ◽

False Negative ◽

Ground Truth ◽

Etiological Diagnosis ◽

Geriatric Unit ◽

Positron Emission ◽

The Brain ◽

Hospitalized Geriatric Patients

Background: Positron emission tomography (PET) of the brain with 2-[F-18]-fluoro-2-deoxy-D-glucose (FDG) is widely used for the etiological diagnosis of clinically uncertain cognitive impairment (CUCI). Acute full-blown delirium can cause reversible alterations of FDG uptake that mimic neurodegenerative disease. Objective: This study tested whether delirium in remission affects the performance of FDG PET for differentiation between neurodegenerative and non-neurodegenerative etiology of CUCI. Methods: The study included 88 patients (82.0±5.7 y) with newly detected CUCI during hospitalization in a geriatric unit. Twenty-seven (31%) of the patients were diagnosed with delirium during their current hospital stay, which, however, at time of enrollment was in remission so that delirium was not considered the primary cause of the CUCI. Cases were categorized as neurodegenerative or non-neurodegenerative etiology based on visual inspection of FDG PET. The diagnosis at clinical follow-up after ≥12 months served as ground truth to evaluate the diagnostic performance of FDG PET. Results: FDG PET was categorized as neurodegenerative in 51 (58%) of the patients. Follow-up after 16±3 months was obtained in 68 (77%) of the patients. The clinical follow-up diagnosis confirmed the FDG PET-based categorization in 60 patients (88%, 4 false negative and 4 false positive cases with respect to detection of neurodegeneration). The fraction of correct PET-based categorization did not differ between patients with delirium in remission and patients without delirium (86% versus 89%, p = 0.666). Conclusion: Brain FDG PET is useful for the etiological diagnosis of CUCI in hospitalized geriatric patients, as well as in patients with delirium in remission.

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