scholarly journals A new 2D‐3D registration gold‐standard dataset for the hip joint based on uncertainty modeling

2021 ◽  
Author(s):  
Fabio D’Isidoro ◽  
Christophe Chênes ◽  
Stephen J. Ferguson ◽  
Jérôme Schmid
Keyword(s):  
Hip Joint ◽  
Gold Standard ◽  
Uncertainty Modeling ◽  
3D Registration ◽  
Gold Standard Dataset

Textual entailment graphs

2015 ◽  
Vol 21 (5) ◽  
pp. 699-724 ◽  
Author(s):  
LILI KOTLERMAN ◽  
IDO DAGAN ◽  
BERNARDO MAGNINI ◽  
LUISA BENTIVOGLI
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Gold Standard ◽  
State Of The Art ◽  
Text Analytics ◽  
Joint Work ◽  
Gold Standard Dataset ◽  
Textual Entailment ◽  
Interesting Task

AbstractIn this work, we present a novel type of graphs for natural language processing (NLP), namely textual entailment graphs (TEGs). We describe the complete methodology we developed for the construction of such graphs and provide some baselines for this task by evaluating relevant state-of-the-art technology. We situate our research in the context of text exploration, since it was motivated by joint work with industrial partners in the text analytics area. Accordingly, we present our motivating scenario and the first gold-standard dataset of TEGs. However, while our own motivation and the dataset focus on the text exploration setting, we suggest that TEGs can have different usages and suggest that automatic creation of such graphs is an interesting task for the community.

scholarly journals Validation for 2D/3D registration II: The comparison of intensity- and gradient-based merit functions using a new gold standard data set

2011 ◽  
Vol 38 (3) ◽  
pp. 1491-1502 ◽  
Author(s):  
Christelle Gendrin ◽  
Primož Markelj ◽  
Supriyanto Ardjo Pawiro ◽  
Jakob Spoerk ◽  
Christoph Bloch ◽  
...  
Keyword(s):  
Gold Standard ◽  
Merit Functions ◽  
3D Registration ◽  
Data Set ◽  
Standard Data ◽  
Gradient Based

scholarly journals A comprehensive benchmarking of WGS-based structural variant callers

2020 ◽  
Author(s):  
Varuni Sarwal ◽  
Sebastian Niehus ◽  
Ram Ayyala ◽  
Sei Chang ◽  
Angela Lu ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Gold Standard ◽  
Detection Methods ◽  
Whole Genome ◽  
Sequencing Data ◽  
Structural Variant ◽  
Gold Standard Dataset ◽  
Low Pass ◽  
Standard Set

AbstractAdvances in whole genome sequencing promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from whole genome sequencing (WGS) data presents a substantial number of challenges and a plethora of SV-detection methods have been developed. Currently, there is a paucity of evidence which investigators can use to select appropriate SV-detection tools. In this paper, we evaluated the performance of SV-detection tools using a comprehensive PCR-confirmed gold standard set of SVs. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of SV-detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance, as the SV-detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV-detection tools varied widely in their performance, with several methods providing a good balance between sensitivity and precision. Additionally, we have determined the SV callers best suited for low and ultra-low pass sequencing data.

Validation of a method to measure three-dimensional hip joint kinematics in subjects with femoroacetabular impingement

2019 ◽  
pp. 112070001988354
Author(s):  
Thomas R Ward ◽  
Mafruha M Hussain ◽  
Mark Pickering ◽  
Diana Perriman ◽  
Al Burns ◽  
...  
Keyword(s):  
Femoroacetabular Impingement ◽  
Hip Joint ◽  
Gold Standard ◽  
Dynamic Assessment ◽  
Three Dimensional ◽  
3D Models ◽  
Joint Kinematics ◽  
Registration Method ◽  
Kinematic Measurement ◽  
Significant Difference

Introduction: A kinematic measurement method combining dynamic motion and imaging, which captures the behaviour of the hip at terminal motion, may offer improved diagnostic accuracy and enhance our understanding of the mechanics of femoroacetabular impingement (FAI). Methods: 3 embalmed cadaveric hip/pelvis specimens with implanted Roentgen Stereophotogrammetric Analysis (RSA) beads were mounted on a custom rig and imaged with a fluoroscope in four poses to simulate a clinical impingement examination: in hip extension and in three positions: near impingement, early impingement and late impingement while simulating a flexion/adduction/internal rotation manoeuvre. Hip joint kinematics were measured using 2 methods and compared: RSA (gold standard) and a custom 3-dimensional to 2-dimensional (3D–2D) image registration method which matches 3D models developed from CT to 2D fluoroscopic images. Results: Using RSA as the gold standard, bias and precision of hip joint rotations measured using 3D–2D registration demonstrated maximums of 1.64° and 3.96°, respectively. However, if the single outlier was removed, bias and precision were 0.55° and 1.38°. Bias and precision of translations had maximums of 0.51 mm and 0.77 mm, respectively. Conclusions: This 3D to 2D registration method may offer a clinically useful solution for dynamic assessment of hip impingement. If 5-mm translation and 10° of rotation represent a clinically significant difference in hip kinematics, the method’s accuracy of approximately 1 mm displacement and 1° rotation should enable detection of significant clinical differences.

US assessment of hip joint synovitis in rheumatic diseases: A comparison with MR imaging

2003 ◽  
Vol 44 (1) ◽  
pp. 72-78
Author(s):  
I. Soini ◽  
A. Kotaniemi ◽  
H. Kautiainen ◽  
M. Kauppi
Keyword(s):  
Mr Imaging ◽  
Rheumatic Disease ◽  
Rheumatic Diseases ◽  
Hip Joint ◽  
False Positive ◽  
Gold Standard ◽  
Intraclass Correlation ◽  
Hip Joints ◽  
Mri Evaluation ◽  
Rheumatic Patients

Purpose: To assess the significance of ultrasonography (US) in detecting hip joint synovitis in patients with rheumatic diseases. Material and Methods: Forty patients with rheumatic disease and suspected hip joint synovitis underwent MRI and US of the hip joint. In addition to the throughout MRI evaluation, the anterior collum-capsule distance (CCD) was determined by both MRI and US. Thirteen healthy volunteers were examined with MRI to establish the criteria for normal findings in MRI when classifying hip joints to those with synovitis and those without. MRI was used as a gold standard. Results: Synovitis was found using MRI in 31 hips of 22 patients (9 patients had bilateral synovitis). The intraclass correlation was 0.61 between MRI and US in measuring CCD. In classifying hip joint synovitis with US, the sensitivity of the method was 87% and specificity 42%, when the CCD criterion for synovitis was determined to be ≥7 mm. If the cut-off point was raised to 9 mm, the sensitivity decreased to 61% while specificity increased to 94%. A difference in CCD of ≥1 mm between the hips as an additional criterion for synovitis increased the number of false-positive findings. Conclusion: Measurement of CCD with US proved to be a rather inaccurate method to point out synovitis in rheumatic patients when using MRI as a reference. The main reason for this result was the thickened capsule, which US could not differentiate from a thickened synovium.

scholarly journals NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition

2021 ◽  
pp. 103779
Author(s):  
Rezarta Islamaj ◽  
Chih-Hsuan Wei ◽  
David Cissel ◽  
Nicholas Miliaras ◽  
Olga Printseva ◽  
...  
Keyword(s):  
Gold Standard ◽  
Gene Recognition ◽  
Gold Standard Dataset

scholarly journals Automatic Assessment of Absolute Sentence Complexity

2017 ◽  
Author(s):  
Sanja Stajner ◽  
Simone Paolo Ponzetto ◽  
Heiner Stuckenschmidt
Keyword(s):  
Gold Standard ◽  
Reading Time ◽  
Classification Performance ◽  
Automatic Assessment ◽  
Sentence Complexity ◽  
Gold Standard Dataset ◽  
Corpus Size ◽  
Complexity Assessment ◽  
The Impact ◽  
Robust Systems

Lexically and syntactically simpler sentences result in shorter reading time and better understanding in many people. However, no reliable systems for automatic assessment of absolute sentence complexity have been proposed so far. Instead, the assessment is usually done manually, requiring expert human annotators. To address this problem, we first define the sentence complexity assessment as a five-level classification task, and build a ‘gold standard’ dataset. Next, we propose robust systems for sentence complexity assessment, using a novel set of features based on leveraging lexical properties of freely available corpora, and investigate the impact of the feature type and corpus size on the classification performance.

scholarly journals The Automatic Detection of Dataset Names in Scientific Articles

Data ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 84
Author(s):  
Jenny Heddes ◽  
Pim Meerdink ◽  
Miguel Pieters ◽  
Maarten Marx
Keyword(s):  
Open Access ◽  
Gold Standard ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Rule Based ◽  
Named Entity ◽  
Gold Standard Dataset ◽  
The Many ◽  
Better Than

We study the task of recognizing named datasets in scientific articles as a Named Entity Recognition (NER) problem. Noticing that available annotated datasets were not adequate for our goals, we annotated 6000 sentences extracted from four major AI conferences, with roughly half of them containing one or more named datasets. A distinguishing feature of this set is the many sentences using enumerations, conjunctions and ellipses, resulting in long BI+ tag sequences. On all measures, the SciBERT NER tagger performed best and most robustly. Our baseline rule based tagger performed remarkably well and better than several state-of-the-art methods. The gold standard dataset, with links and offsets from each sentence to the (open access available) articles together with the annotation guidelines and all code used in the experiments, is available on GitHub.

scholarly journals Integration of probabilistic functional networks without an external Gold Standard

2021 ◽  
Author(s):  
Katherine James ◽  
Aoesha Alsobhe ◽  
Simon Joseph Cockell ◽  
Anil Wipat ◽  
Matthew Pocock
Keyword(s):  
Protein Function ◽  
Gold Standard ◽  
Network Performance ◽  
Protein Function Prediction ◽  
Quality Data ◽  
Integrated Networks ◽  
Data Redundancy ◽  
Gold Standard Dataset ◽  
Comparable Performance ◽  
Using Data

Background: Probabilistic functional integrated networks (PFINs) are designed to aid our understanding of cellular biology and can be used to generate testable hypotheses about protein function. PFINs are generally created by scoring the quality of interaction datasets against a Gold Standard dataset, usually chosen from a separate high-quality data source, prior to their integration. Use of an external Gold Standard has several drawbacks, including data redundancy, data loss and the need for identifier mapping, which can complicate the network build and impact on PFIN performance. Results: We describe the development of an integration technique, ssNet, that scores and integrates both high-throughput and low-throughout data from a single source database in a consistent manner without the need for an external Gold Standard dataset. Using data from Saccharomyces cerevisiae we show that ssNet is easier and faster, overcoming the challenges of data redundancy, Gold Standard bias and ID mapping, while producing comparable performance. In addition ssNet results in less loss of data and produces a more complete network. Conclusions: The ssNet method allows PFINs to be built successfully from a single database, while producing comparable network performance to networks scored using an external Gold Standard source. Keywords: Network integration; Bioinformatics; Gold Standards; Probabilistic functional integrated networks; Protein function prediction; Interactome.

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