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Automating image segmentation verification and validation by learning test oracles
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/spe.520openalex-snapshot
Investigating the use of analysis contracts to improve the testability of object‐oriented code
inferredcites →https://doi.org/10.1007/978-3-540-49830-8openalex-snapshot
Image Processing in Radiology
inferredcites →https://doi.org/10.1007/978-3-540-75757-3_118openalex-snapshot
On Simulating Subjective Evaluation Using Combined Objective Metrics for Validation of 3D Tumor Segmentation
inferredcites →https://doi.org/10.1016/0146-664x(82)90070-3openalex-snapshot
A study of edge detection algorithms
inferredcites →https://doi.org/10.1016/b978-1-55860-377-6.50023-2openalex-snapshot
Fast Effective Rule Induction
inferredcites →https://doi.org/10.1016/c2009-0-19715-5openalex-snapshot
Data Mining: Practical Machine Learning Tools and Techniques
inferredcites →https://doi.org/10.1016/j.compmedimag.2005.12.001openalex-snapshot
A framework for evaluating image segmentation algorithms
inferredcites →https://doi.org/10.1016/j.jss.2008.07.019openalex-snapshot
PAT: A pattern classification approach to automatic reference oracles for the testing of mesh simplification programs
inferredcites →https://doi.org/10.1016/j.neuroimage.2011.01.006openalex-snapshot
A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain segmentation
inferredcites →https://doi.org/10.1017/9781108974073.004openalex-snapshot
Introduction to Software Testing
inferredcites →https://doi.org/10.1017/cbo9780511809163openalex-snapshot
Introduction to Software Testing
inferredcites →https://doi.org/10.1093/comjnl/25.4.465openalex-snapshot
On Testing Non-Testable Programs
inferredcites →https://doi.org/10.1109/12.2185openalex-snapshot
Data diversity: an approach to software fault tolerance
inferredcites →https://doi.org/10.1109/cvpr.2006.185openalex-snapshot
Meta-Evaluation of Image Segmentation Using Machine Learning
inferredcites →https://doi.org/10.1109/icip.2002.1038142openalex-snapshot
Scalable discrepancy measures for segmentation evaluation
inferredcites →https://doi.org/10.1109/icse.1992.753494openalex-snapshot
Specification-based test oracles for reactive systems
inferredcites →https://doi.org/10.1109/proc.1979.11325openalex-snapshot
Quantitative design and evaluation of enhancement/thresholding edge detectors
inferredcites →https://doi.org/10.1109/qsic.2007.4385529openalex-snapshot
The Oracle Problem for Testing against Quantified Properties
inferredcites →https://doi.org/10.1109/tkde.2003.1245283openalex-snapshot
Benchmarking attribute selection techniques for discrete class data mining
inferredcites →https://doi.org/10.1109/tmi.2006.880587openalex-snapshot
Generalized Overlap Measures for Evaluation and Validation in Medical Image Analysis
inferredcites →https://doi.org/10.1109/tpami.2006.233openalex-snapshot
Random Walks for Image Segmentation
inferredcites →https://doi.org/10.1109/tse.1986.6312924openalex-snapshot
An experimental evaluation of the assumption of independence in multiversion programming
inferredcites →https://doi.org/10.1117/1.2348895openalex-snapshot
Book Review: Image Processing: Principles and Applications
inferredcites →https://doi.org/10.1145/143062.143100openalex-snapshot
Specification-based test oracles for reactive systems
inferredcites →https://doi.org/10.1145/1572272.1572295openalex-snapshot
Automatic system testing of programs without test oracles
inferredcites →https://doi.org/10.1145/355744.355745openalex-snapshot
An Algorithm for Finding Best Matches in Logarithmic Expected Time
inferredcites →https://doi.org/10.1145/800175.809889openalex-snapshot
Pseudo-oracles for non-testable programs
inferredcites →https://doi.org/10.1146/annurev.bioeng.2.1.315openalex-snapshot
Current Methods in Medical Image Segmentation
inferredcites →https://doi.org/10.22215/etd/2009-09134openalex-snapshot
Learning a test Oracle towards automating image segmentation evaluation
inferredcites →https://doi.org/10.2967/jnumed.108.052787openalex-snapshot
Image Processing in Radiology: Current Applications
inferredcites →https://doi.org/10.4018/978-1-59140-753-9openalex-snapshot
Advances in Image and Video Segmentation
inferredcites →https://doi.org/10.4018/978-1-59140-753-9.ch018openalex-snapshot
Unsupervised and Supervised Image Segmentation Evaluation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Predicting metamorphic relations for testing scientific software: a machine learning approach using graph kernels
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Testing Research Software: A Case Study
inferred← citeshttps://doi.org/10.1007/978-3-642-33415-3_65openalex-snapshot
Evaluating Segmentation Error without Ground Truth
inferred← citeshttps://doi.org/10.1007/978-981-19-9948-2_8openalex-snapshot
Artificial Intelligence Techniques in System Testing
inferred← citeshttps://doi.org/10.1007/s11219-017-9392-4openalex-snapshot
A mapping study on testing non-testable systems
inferred← citeshttps://doi.org/10.1016/b978-0-12-800160-8.00001-2openalex-snapshot
Automated Test Oracles
inferred← citeshttps://doi.org/10.1016/b978-0-12-800160-8.00003-6openalex-snapshot
Automated Test Oracles
inferred← citeshttps://doi.org/10.1016/b978-0-32-391913-5.00013-0openalex-snapshot
Basic software testing concepts
inferred← citeshttps://doi.org/10.1016/b978-0-443-29242-2.00007-0openalex-snapshot
Sourcing good data: from image acquisition to segmentation
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.106422openalex-snapshot
TMS-Net: A segmentation network coupled with a run-time quality control method for robust cardiac image segmentation
inferred← citeshttps://doi.org/10.1016/j.dam.2015.06.002openalex-snapshot
A new measure for comparing biomedical regions of interest in segmentation of digital images
inferred← citeshttps://doi.org/10.1016/j.media.2017.07.004openalex-snapshot
Designing image segmentation studies: Statistical power, sample size and reference standard quality
inferred← citeshttps://doi.org/10.1038/s41598-020-77733-4openalex-snapshot
Automatic segmentation with detection of local segmentation failures in cardiac MRI
inferred← citeshttps://doi.org/10.1109/ibcast.2016.7429896openalex-snapshot
A framework of automatic testing of image processing applications
inferred← citeshttps://doi.org/10.1109/icsess.2015.7339246openalex-snapshot
Automatic test Oracle for image processing applications using support vector machines
inferred← citeshttps://doi.org/10.1109/icsm.2011.6080766openalex-snapshot
Useful software engineering research - leading a double-agent life
inferred← citeshttps://doi.org/10.1109/icst53961.2022.00015openalex-snapshot
Metamorphic Fuzzing of C++ Libraries
inferred← citeshttps://doi.org/10.1109/issre.2013.6698899openalex-snapshot
Using machine learning techniques to detect metamorphic relations for programs without test oracles
inferred← citeshttps://doi.org/10.1109/ms.2017.34openalex-snapshot
Test Automation: Not Just for Test Execution
inferred← citeshttps://doi.org/10.1109/secse.2013.6615099openalex-snapshot
Techniques for testing scientific programs without an oracle
inferred← citeshttps://doi.org/10.1109/tmi.2017.2665165openalex-snapshot
Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
inferred← citeshttps://doi.org/10.1109/tse.2014.2372785openalex-snapshot
The Oracle Problem in Software Testing: A Survey
inferred← citeshttps://doi.org/10.1117/1.jmi.1.3.034005openalex-snapshot
On the evaluation of segmentation editing tools
inferred← citeshttps://doi.org/10.1145/2970276.2970366openalex-snapshot
Supporting oracle construction via static analysis
inferred← citeshttps://doi.org/10.1145/3524481.3527232openalex-snapshot
A method and experiment to evaluate deep neural networks as test oracles for scientific software
inferred← citeshttps://doi.org/10.1145/3540250.3549086openalex-snapshot
Perfect is the enemy of test oracle
inferred← citeshttps://doi.org/10.11606/d.55.2018.tde-23102018-175603openalex-snapshot
Automatização de oráculos de teste para imagens médicas de modelos tridimensionais
inferred← citeshttps://doi.org/10.3310/kpwq4208openalex-snapshot
Development and evaluation of machine-learning methods in whole-body magnetic resonance imaging with diffusion weighted imaging for staging of patients with cancer: the MALIBO diagnostic test accuracy study
inferred← citeshttps://doi.org/10.4018/ijsir.2022010102openalex-snapshot
Optimization-Based Tuberculosis Image Segmentation by Ant Colony Heuristic Method
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Metaheuristic-Based Optimization Methods for the Segmentation of Tuberculosis Sputum Smear Images
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