Estimating false positive and false negative error rates in cervical cell classification.

The performance of a cell recognition system on unknown data is often estimated in terms of its error rates on a test set. This paper investigates methods for producing estimates of error rates in cervical cell classification. Classification performance curves calculated using these methods are given for several classification schemes used to classify 1500 cervical cells.

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High resolution analysis of cervical cells--a progress report.

Journal of Histochemistry & Cytochemistry ◽

10.1177/25.7.330722 ◽

1977 ◽

Vol 25 (7) ◽

pp. 689-695 ◽

Cited By ~ 11

Author(s):

R S Poulsen ◽

L H Oliver ◽

R L Cahn ◽

C Louis ◽

G Toussaint

Keyword(s):

High Resolution ◽

False Positive ◽

False Negative ◽

Error Rates ◽

Cell Segmentation ◽

Negative Error ◽

False Negative Error ◽

High Resolution Analysis ◽

Resolution Analysis ◽

Cervical Cells

This paper presents preliminary results of research toward the development of a high resolution analysis stage for a dual resolution image processing-based prescreening device for cervical cytology. Experiments using both manual and automatic methods for cell segmentation are described. In both cases, 1500 cervical cells were analyzed and classified as normal or abnormal (dysplastic or malignant) using a minimum Mahalanobis distance classifier with eight subclasses of normal cells, and five subclasses of abnormal cells. With manual segmentation, false positive and false negative error rates of 2.98 and 7.73% were obtained. Similar experiments using automatic cell segmentation methods yielded false positive and false negative error rates of 3.90 and 11.56%, respectively. In both cases, independent training and testing data were used.

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A Study of False-Positive and False-Negative Error Rates in Cartridge Case Comparisons

10.21236/ada611807 ◽

2014 ◽

Cited By ~ 2

Author(s):

David P. Baldwin ◽

Stanley J. Bajic ◽

Max Morris ◽

Daniel Zamzow

Keyword(s):

False Positive ◽

False Negative ◽

Error Rates ◽

Negative Error ◽

False Negative Error ◽

Cartridge Case

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Markovian analysis of cervical cell images.

Journal of Histochemistry & Cytochemistry ◽

10.1177/24.1.56387 ◽

1976 ◽

Vol 24 (1) ◽

pp. 138-144 ◽

Cited By ~ 74

Author(s):

N J Pressman

Keyword(s):

Transition Probabilities ◽

Classification Performance ◽

Error Rates ◽

Classification Error ◽

Correct Classification ◽

Cell Nuclei ◽

Step Size ◽

Cervical Cell ◽

Texture Parameters ◽

Markovian Analysis

Markovian analysis is a method to measure optical texture based on gray-level transition probabilities in digitized images. Experiments are described that investigate that classification performance of parameters generated by Markovian analysis. Results using Markov texture parameters show that the selection of a Markov step size strongly affects classification error rates and the number of parameters required to achieve the maximum correct classification rates. Markov texture parameters are shown to achieve high rates of correct classification in discriminating images of normal from abnormal cervical cell nuclei.

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Error Rates in Competency Testing When Test Retaking Is Permitted

Journal of Educational Statistics ◽

10.3102/10769986015001039 ◽

1990 ◽

Vol 15 (1) ◽

pp. 39-52 ◽

Cited By ~ 1

Author(s):

Huynh Huynh

Keyword(s):

Conditional Probability ◽

False Positive ◽

False Negative ◽

Error Rates ◽

Rasch Models ◽

Positive Error ◽

False Positive Error ◽

Competency Testing ◽

Negative Error ◽

False Negative Error

False positive and false negative error rates are studied for competency testing where examinees are permitted to retake the test if they fail to pass. Formulae are provided for the beta-binomial and Rasch models, and estimates based on these two models are compared for several typical situations. Although Rasch estimates are expected to be more accurate than beta-binomial estimates, differences among them are found not to be substantial in a number of practical situations. Under relatively general conditions and when test retaking is permitted, the probability of making a false negative error is zero. Under the same situation, and given that an examinee is a true nonmaster, the conditional probability of making a false positive error for this examinee is one.

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Errors in prostate core biopsy diagnosis in an era of specialisation and double reporting

Journal of Clinical Pathology ◽

10.1136/jclinpath-2020-206726 ◽

2020 ◽

pp. jclinpath-2020-206726

Author(s):

Cornelia Margaret Szecsei ◽

Jon D Oxley

Keyword(s):

Error Rate ◽

False Positive ◽

Core Biopsy ◽

False Negative ◽

Diagnostic Error ◽

Error Rates ◽

False Positive Error ◽

Biopsy Diagnosis ◽

False Negative Error ◽

False Positive Error Rate

AimTo examine the effects of specialist reporting on error rates in prostate core biopsy diagnosis.MethodBiopsies were reported by eight specialist uropathologists over 3 years. New cancer diagnoses were double-reported and all biopsies were reviewed for the multidisciplinary team (MDT) meeting. Diagnostic alterations were recorded in supplementary reports and error rates were compared with a decade previously.Results2600 biopsies were reported. 64.1% contained adenocarcinoma, a 19.7% increase. The false-positive error rate had reduced from 0.4% to 0.06%. The false-negative error rate had increased from 1.5% to 1.8%, but represented fewer absolute errors due to increased cancer incidence.ConclusionsSpecialisation and double-reporting have reduced false-positive errors. MDT review of negative cores continues to identify a very low number of false-negative errors. Our data represents a ‘gold standard’ for prostate biopsy diagnostic error rates. Increased use of MRI-targeted biopsies may alter error rates and their future clinical significance.

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Smart Pooling: AI-Powered COVID-19 Informative Group Testing

10.21203/rs.3.rs-805716/v1 ◽

2021 ◽

Author(s):

Maria Escobar ◽

Guillaume Jeanneret ◽

Laura Bravo-Sánchez ◽

Angela Castillo ◽

Catalina Gómez ◽

...

Keyword(s):

Machine Learning ◽

False Negative ◽

Group Testing ◽

Error Rates ◽

Molecular Testing ◽

Proof Of Concept ◽

Efficiency Gains ◽

Automated Method ◽

Sociodemographic Data ◽

False Negative Error

Abstract Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8,000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.

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Grandparent inference from genetic data: The potential for parentage-based tagging programs to identify offspring of hatchery strays

10.1101/2021.08.11.455946 ◽

2021 ◽

Author(s):

Thomas A Delomas ◽

Matthew Campbell

Keyword(s):

False Negative ◽

Genetic Data ◽

R Package ◽

Error Rates ◽

Nucleotide Polymorphisms ◽

Negative Effects ◽

Single Nucleotide ◽

Natural Origin ◽

False Negative Error ◽

In The Wild

Fisheries managers routinely use hatcheries to increase angling opportunity. Many hatcheries operate as segregated programs where hatchery-origin fish are not intended to spawn with natural-origin conspecifics in order to prevent potential negative effects on the natural-origin population. Currently available techniques to monitor the frequency with which hatchery-origin strays successfully spawn in the wild rely on either genetic differentiation between the hatchery- and natural-origin fish or extensive sampling of fish on the spawning grounds. We present a method to infer grandparent-grandchild trios using only genotypes from two putative grandparents and one putative grandchild. We developed estimators of false positive and false negative error rates and showed that genetic panels containing 500 - 700 single nucleotide polymorphisms or 200 - 300 microhaplotypes are expected to allow application of this technique for monitoring segregated hatchery programs. We discuss the ease with which this technique can be implemented by pre-existing parentage-based tagging programs and provide an R package that applies the method.

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