Estimating the uncertainty in the estimated mean area under the ROC curve of a classifier

Yousef, Waleed A.; Wagner, Robert F.; Loew, Murray H.

doi:10.1016/j.patrec.2005.06.006

Cited by 33 publications

(44 citation statements)

References 9 publications

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“…In addition, the estimated standard errors of the estimated AUCs refl ect only the fi nite size of the testing set. A future study of interest would be to further assess the performance variability due to the fi nite size of the training set, which characterizes the stability of the classifi er with respect to varying training sets ( 48,49 ).…”

Section: Breast Imaging: Computerized Prognostic Characterization Of mentioning

confidence: 99%

Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers

Bhooshan¹,

Giger²,

Jansen³

et al. 2010

Radiology

177

155

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Purpose:To assess the performance of computer-extracted dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging kinetic and morphologic features in the differentiation of invasive versus noninvasive breast lesions and metastatic versus nonmetastatic breast lesions. Materials and Methods:In this institutional review board-approved HIPAA-compliant study, in which the requirement for informed patient consent was waived, breast MR images were retrospectively collected. The images had been obtained with a 1.5-T MR unit by using a gadodiamide-enhanced T1-weighted spoiled gradient-recalled acquisition in the steady state sequence. The breast MR imaging database contained 132 benign, 71 ductal carcinoma in situ (DCIS), and 150 invasive ductal carcinoma (IDC) lesions. Fifty-four IDC lesions were associated with metastasis-positive lymph nodes (LNs), and 64 IDC lesions were associated with negative LNs. Lesion segmentation and extraction of morphologic and kinetic features were automatically performed by a laboratory-developed computer workstation. Features were fi rst selected by using stepwise linear discriminant analysis and then merged by using Bayesian neural networks. Lesion classifi cation performance was assessed with receiver operating characteristic analysis. Results: Conclusion:Computer-aided diagnosis of breast DCE MR imagingdepicted lesions was extended from the task of discriminating between malignant and benign lesions to the prognostic tasks of distinguishing between noninvasive and invasive lesions and discriminating between metastatic and nonmetastatic lesions, yielding MR imaging-based prognostic markers.q RSNA, 2010 Supplemental material: http://radiology.rsna.org/lookup/ suppl

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Section: Breast Imaging: Computerized Prognostic Characterization Of mentioning

confidence: 99%

Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers

Bhooshan¹,

Giger²,

Jansen³

et al. 2010

Radiology

177

155

View full text Add to dashboard Cite

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“…1), and (ii) the stability of a classifier with respect to varying training datasets, for which Bob Wagner pioneered the notion of "training variability" analogous to the "reader variability" in MRMC reader studies. 7,8 There are two interesting CAD papers presented in this special section for diagnosis of Alzheimer's disease and breast cancer risk prediction, respectively. Both are exploratory studies with limited datasets using cross-validation for classifier training and validation.…”

mentioning

confidence: 99%

Special Section Guest Editorial: Pioneers in Medical Imaging: Honoring the Memory of Robert F. Wagner

Myers

Chen

2014

J. Med. Imag

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“…In [37], authors describe the ROC curves as a metric that measures the method's performance in a more generic way than the error rate. The authors proved that it is possible to obtain very little bias even for small sample estimates.…”

Section: Application Of Performance Metrics In Medicinementioning

confidence: 99%

A Survey on Data Mining Algorithms and Techniques in Medicine

Madadipouya

2017

JOIV : Int. J. Inform. Visualization

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Abstract-Medical Decision Support Systems (MDSS) industry collects a huge amount of data, which is not properly mined and not put to the optimum use. This data may contain valuable information that awaits extraction. The knowledge may be encapsulated in various patterns and regularities that may be hidden in the data. Such knowledge may prove to be priceless in future medical decision making. Available medical decision support systems are based on static data, which may be out of date. Thus, a medical decision support system that can learn the relationships between patient histories, diseases in the population, symptoms, pathology of a disease, family history, and test results, would be useful to physicians and hospitals.

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Estimating the uncertainty in the estimated mean area under the ROC curve of a classifier

Cited by 33 publications

References 9 publications

Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers

Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers

Special Section Guest Editorial: Pioneers in Medical Imaging: Honoring the Memory of Robert F. Wagner

A Survey on Data Mining Algorithms and Techniques in Medicine

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