Recent developments in imaging system assessment methodology, FROC analysis and the search model
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Summary
Progress is reviewed in the free-response ROC (FROC) paradigm in which the observer marks and rates suspicious regions and the location information is used to determine whether lesions were correctly localized.
- Type
- article
- Published
- 2011-08-21
- Cited by
- 22
- References
- 25
- Access
- Open access
- OpenAlex
- https://openalex.org/W2082384201
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:33946283
Keywords
Computer science, Receiver operating characteristic, Artificial intelligence, Context (archaeology), Observer (physics)
References
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- The "proper" binormal model: parametric receiver operating characteristic curve estimation with degenerate data.
- Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis.
- Reliable and computationally efficient maximum-likelihood estimation of "proper" binormal ROC curves.
- A search model and figure of merit for observer data acquired according to the free-response paradigm
- Spatial localization accuracy of radiologists in free-response studies: Inferring perceptual FROC curves from mark-rating data.
- The FROC curve: a representation of the observer's performance for the method of free response.
- ROC Methodology in Radiologic Imaging
- Some practical issues of experimental design and data analysis in radiological ROC studies.
- Validation and statistical power comparison of methods for analyzing free-response observer performance studies
- Data analysis for detection and localization of multiple abnormalities with application to mammography.
- Maximum-likelihood estimation of parameters of signal-detection theory and determination of confidence intervals—Rating-method data
- Operating characteristics predicted by models for diagnostic tasks involving lesion localization.
- A Free Response Approach To The Measurement And Characterization Of Radiographic Observer Performance
- Observer studies involving detection and localization: modeling, analysis, and validation.
- Receiver operating characteristic rating analysis. Generalization to the population of readers and patients with the jackknife method.
Cited by
- A brief history of FROC paradigm data analysis
- The Value of Observer Performance Studies in Dose Optimization: A Focus on Free-Response Receiver Operating Characteristic Methods*
- The influence of experience and training in a group of novice observers: A jackknife alternative free-response receiver operating characteristic analysis
- New Developments in Observer Performance Methodology in Medical Imaging
- The role of the free-response receiver operating characteristic method for dose and image quality optimisation
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
- Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs.
- Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing
- Deep learning based automatic liver tumor segmentation in CT with shape-based post-processing
- Evaluation of pseudo-reader study designs to estimate observer performance results as an alternative to fully crossed, multi-reader, multi-case studies
- Detectability of small objects in PET/computed tomography phantom images with Bayesian penalised likelihood reconstruction
- Extravalidation and reproducibility results of a commercial deep learning‐based automatic detection algorithm for pulmonary nodules on chest radiographs at tertiary hospital
- Undetected Lung Cancer at Posteroanterior Chest Radiography: Potential Role of a Deep Learning-based Detection Algorithm.
- Value of a deep learning-based algorithm for detecting Lung-RADS category 4 nodules on chest radiographs in a health checkup population: estimation of the sample size for a randomized controlled trial
- Generating synthetic contrast enhancement from non-contrast chest computed tomography using a generative adversarial network
- Domain adaptation strategies for cancer-independent detection of lymph node metastases
- Generalization of Deep Learning in Digital Pathology: Experience in Breast Cancer Metastasis Detection
- Continual learning strategies for cancer-independent detection of lymph node metastases
- Optimized use of MRI in a PSA- based prostate cancer screening program
- Overview of the Receiver Operating Characteristic ( ROC ) Paradigm
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