0000000000879883

AUTHOR

Pieter Wesseling

showing 4 related works from this author

Ranking of Brain Tumour Classifiers Using a Bayesian Approach

2009

This study presents a ranking for classifers using a Bayesian perspective. This ranking framework is able to evaluate the performance of the models to be compared when they are inferred from different sets of data. It also takes into account the performance obtained on samples not used during the training of the classifiers. Besides, this ranking assigns a prior to each model based on a measure of similarity of the training data to a test case. An evaluation consisting of ranking brain tumour classifiers is presented. These multilayer perceptron classifiers are trained with 1H magnetic resonance spectroscopy (MRS) signals following a multiproject multicenter evaluation approach. We demonstr…

Measure (data warehouse)Training setComputer sciencebusiness.industryPerspective (graphical)Bayesian probabilityPattern recognitionMachine learningcomputer.software_genreRanking (information retrieval)Random subspace methodSimilarity (network science)Multilayer perceptronArtificial intelligencebusinesscomputer
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Incremental Gaussian Discriminant Analysis based on Graybill and Deal weighted combination of estimators for brain tumour diagnosis

2011

In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally…

Graybill-Deal estimatorDatabases FactualComputer sciencePopulation-based incremental learningGaussianTraining setsHealth InformaticsMachine learningcomputer.software_genreIncremental algorithmPersonalizationsymbols.namesakeAutomatic brain tumour diagnosisArtificial IntelligenceNumber of samplesMachine learningMagnetic resonance spectroscopyHumansPreprocessIncremental learningTraining setbusiness.industryBrain NeoplasmsBrain tumoursEstimatorComputational BiologyPattern recognitionLinear discriminant analysisMagnetic Resonance ImagingDiscriminant analysisTranslational research Tissue engineering and pathology [ONCOL 3]Graybill–Deal estimatorComputer Science ApplicationsGaussiansMagnetic resonanceFISICA APLICADAIncremental learningsymbolsEmpirical resultsArtificial intelligencebusinessClassifier (UML)computerEstimationAlgorithmsJournal of Biomedical Informatics
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Accurate classification of childhood brain tumours by in vivo H-1 MRS - A multi-centre study

2013

Aims: To evaluate the accuracy of single-voxel Magnetic Resonance Spectroscopy (1H-MRS) as a non-invasive diagnostic aid for pediatric brain tumours in a multi-national study. Our hypotheses are (1) that automated classification based on 1H-MRS provides an accurate non-invasive diagnosis in multi-centre datasets and (2) using a protocol which increases the metabolite information improves the diagnostic accuracy. Methods: 78 patients under 16 years old with histologically proven brain tumours from 10 international centres were investigated. Discrimination of 29 medulloblastomas, 11 ependymomas and 38 pilocytic astrocytomas was evaluated. Single-voxel MRS was undertaken prior to diagnosis (1.…

Cancer ResearchPathologymedicine.medical_specialtyClinical assessmentPilocytic AstrocytomasDiagnostic accuracyDiagnostic aidIn vivo1H MRSPattern recognitionNon-invasive diagnosismedicineMulti centrePre-surgery diagnosis assessmentbusiness.industryEcho timeLinear discriminant analysisClassificationTranslational research Tissue engineering and pathology [ONCOL 3]Multi-centre studyOncologyFISICA APLICADAFeature extractionPaediatric brain tumoursStimulated echoNuclear medicinebusinessEuropean Journal of Cancer
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5th International Symposium on Focused Ultrasound

2016

Introduction Breast fibroadenomata (FAD) are benign lesions which occur in about 10 % of all women. Diagnosis is made by triple assessment (physical examination, imaging and/or histopathology/cytology). For a definitive diagnosis of FAD, the treatment is conservative unless the patient is symptomatic. For symptomatic patients, the lumps can be surgically excised or removed interventionally by vacuum-assisted mammotomy (VAM). Ablative techniques like high-intensity focused ultrasound (HIFU), cryo-ablation and laser ablation have also been used for the treatment of FAD, providing a minimally invasive treatment without scarring or poor cosmesis. This review summarises current trials using mini…

lcsh:Medical physics. Medical radiology. Nuclear medicineFibroadenomataCryo-ablationHigh-intensity focused ultrasoundAblative techniqueslcsh:R895-920ReviewMeeting AbstractsLaser ablationJournal of Therapeutic Ultrasound
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