Deep-learning model observer for a low-contrast hepatic metastases localization task in computed tomography.
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C. McCollough | Lifeng Yu | S. Leng | J. Fletcher | H. Gong | J. Heiken | Michael L. Wells | Michael L Wells
[1] Qiyuan Hu,et al. Deep-learning-based model observer for a lung nodule detection task in computed tomography , 2020, Journal of medical imaging.
[2] Young-Wook Choi,et al. Deep learning model observer for 4-alternative forced choice in digital breast tomosynthesis , 2020 .
[3] Hilde Bosmans,et al. Deep learning channelized Hotelling observer for multi-vendor DBT system image quality evaluation , 2020, Medical Imaging.
[4] Craig K. Abbey,et al. Foveated model observer to predict human search performance on virtual digital breast tomosynthesis phantoms , 2020, Medical Imaging.
[5] Byeongjoon Kim,et al. A Convolutional Neural Network-Based Anthropomorphic Model Observer for Signal Detection in Breast CT Images Without Human-Labeled Data , 2020, IEEE Access.
[6] Baiyu Chen,et al. Localization of liver lesions in abdominal CT imaging: II. Mathematical model observer performance correlates with human observer performance for localization of liver lesions in abdominal CT imaging , 2019, Physics in medicine and biology.
[7] Baiyu Chen,et al. Localization of liver lesions in abdominal CT imaging: I. Correlation of human observer performance between anatomical and uniform backgrounds , 2019, Physics in medicine and biology.
[8] Hua Li,et al. Approximating the Ideal Observer and Hotelling Observer for Binary Signal Detection Tasks by Use of Supervised Learning Methods , 2019, IEEE Transactions on Medical Imaging.
[9] Anaïs Viry,et al. Channelized Hotelling observer correlation with human observers for low-contrast detection in liver CT images , 2019, Journal of medical imaging.
[10] Wei Zhou,et al. A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT. , 2019, Medical physics.
[11] Premkumar Elangovan,et al. Using transfer learning for a deep learning model observer , 2019, Medical Imaging.
[12] K. Togashi,et al. Imaging Characteristics of Liver Metastases Overlooked at Contrast-Enhanced CT. , 2019, AJR. American journal of roentgenology.
[13] Lifeng Yu,et al. Correlation between a deep-learning-based model observer and human observer for a realistic lung nodule localization task in chest CT , 2019, Medical Imaging.
[14] Felix K. Kopp,et al. CNN as model observer in a liver lesion detection task for x‐ray computed tomography: A phantom study , 2018, Medical physics.
[15] Premkumar Elangovan,et al. A deep learning model observer for use in alterative forced choice virtual clinical trials , 2018, Medical Imaging.
[16] Matthew A Kupinski,et al. Correlation between a 2D channelized Hotelling observer and human observers in a low‐contrast detection task with multislice reading in CT , 2017, Medical physics.
[17] Shuai Leng,et al. Estimation of Observer Performance for Reduced Radiation Dose Levels in CT: Eliminating Reduced Dose Levels That Are Too Low Is the First Step. , 2017, Academic radiology.
[18] Francesc Massanes,et al. Evaluation of CNN as anthropomorphic model observer , 2017, Medical Imaging.
[19] Miguel P Eckstein,et al. Foveated model observers to predict human performance in 3D images , 2017, Medical Imaging.
[20] Qiyuan Hu,et al. A virtual clinical trial using projection-based nodule insertion to determine radiologist reader performance in lung cancer screening CT , 2017, Medical Imaging.
[21] R. Morin,et al. U.S. Diagnostic Reference Levels and Achievable Doses for 10 Adult CT Examinations. , 2017, Radiology.
[22] Chi Wan Koo,et al. Evaluation of a projection-domain lung nodule insertion technique in thoracic CT , 2016, SPIE Medical Imaging.
[23] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Shuai Leng,et al. Lesion insertion in the projection domain: Methods and initial results. , 2015, Medical physics.
[25] F O Bochud,et al. Image quality in CT: From physical measurements to model observers. , 2015, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.
[26] W J H Veldkamp,et al. Low contrast detectability performance of model observers based on CT phantom images: kVp influence. , 2015, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.
[27] Michael A. Bruno,et al. Understanding and Confronting Our Mistakes: The Epidemiology of Error in Radiology and Strategies for Error Reduction. , 2015, Radiographics : a review publication of the Radiological Society of North America, Inc.
[28] Shuai Leng,et al. Assessment of Low-Contrast Resolution for the American College of Radiology Computed Tomographic Accreditation Program: What Is the Impact of Iterative Reconstruction? , 2015, Journal of computer assisted tomography.
[29] Mitsuru Ikeda,et al. Lung nodule detection performance in five observers on computed tomography (CT) with adaptive iterative dose reduction using three-dimensional processing (AIDR 3D) in a Japanese multicenter study: Comparison between ultra-low-dose CT and low-dose CT by receiver-operating characteristic analysis. , 2015, European journal of radiology.
[30] Yi Zhang,et al. Degradation of CT Low-Contrast Spatial Resolution Due to the Use of Iterative Reconstruction and Reduced Dose Levels. , 2015, Radiology.
[31] Shuai Leng,et al. Lesion insertion in projection domain for computed tomography image quality assessment , 2015, Medical Imaging.
[32] Ehsan Samei,et al. An Improved Index of Image Quality for Task-based Performance of CT Iterative Reconstruction across Three Commercial Implementations. , 2015, Radiology.
[33] Ehsan Samei,et al. Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE. , 2014, Medical physics.
[34] H C Gifford. Efficient visual-search model observers for PET. , 2014, The British journal of radiology.
[35] W J H Veldkamp,et al. Comparison between human and model observer performance in low-contrast detection tasks in CT images: application to images reconstructed with filtered back projection and iterative algorithms. , 2014, The British journal of radiology.
[36] J. Shindoh,et al. Three-dimensional volumetry in 107 normal livers reveals clinically relevant inter-segment variation in size. , 2014, HPB : the official journal of the International Hepato Pancreato Biliary Association.
[37] R. Brereton,et al. Partial least squares discriminant analysis: taking the magic away , 2014 .
[38] Ke Li,et al. Statistical model based iterative reconstruction (MBIR) in clinical CT systems: experimental assessment of noise performance. , 2014, Medical physics.
[39] Howard C Gifford. A visual-search model observer for multislice-multiview SPECT images. , 2013, Medical physics.
[40] Trafton Drew,et al. Scanners and drillers: characterizing expert visual search through volumetric images. , 2013, Journal of vision.
[41] Shuai Leng,et al. Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: impact of radiation dose and reconstruction algorithms. , 2013, Medical physics.
[42] M. Shiung,et al. Development and Validation of a Practical Lower-Dose-Simulation Tool for Optimizing Computed Tomography Scan Protocols , 2012, Journal of computer assisted tomography.
[43] Adam Wunderlich,et al. A Nonparametric Procedure for Comparing the Areas Under Correlated LROC Curves , 2012, IEEE Transactions on Medical Imaging.
[44] Shuai Leng,et al. Correlation between model observer and human observer performance in CT imaging when lesion location is uncertain , 2012, Medical Imaging.
[45] Grace J Gang,et al. Analysis of Fourier-domain task-based detectability index in tomosynthesis and cone-beam CT in relation to human observer performance. , 2011, Medical physics.
[46] L. Fayad,et al. Soft-tissue masses and masslike conditions: what does CT add to diagnosis and management? , 2010, AJR. American journal of roentgenology.
[47] G. Moneta. Radiation Dose Associated With Common Computed Tomography Examinations and the Associated Lifetime Attributable Risk of Cancer , 2010 .
[48] D. Miglioretti,et al. Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. , 2009, Archives of internal medicine.
[49] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[50] Jia Deng,et al. A large-scale hierarchical image database , 2009, CVPR 2009.
[51] Lucretiu M Popescu,et al. Nonparametric ROC and LROC analysis. , 2007, Medical physics.
[52] Richard Simon,et al. Bias in error estimation when using cross-validation for model selection , 2006, BMC Bioinformatics.
[53] Wenjiang J. Fu,et al. Estimating misclassification error with small samples via bootstrap cross-validation , 2005, Bioinform..
[54] E. Samei,et al. Effects of Anatomical Structure on Signal Detection , 2000 .
[55] E Samei,et al. Detection of subtle lung nodules: relative influence of quantum and anatomic noise on chest radiographs. , 1999, Radiology.
[56] Francis R. Verdun,et al. Importance of anatomical noise in mammography , 1997, Medical Imaging.
[57] R. Swensson. Unified measurement of observer performance in detecting and localizing target objects on images. , 1996, Medical physics.
[58] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[59] R D Nawfel,et al. Contrast-detail curves for liver CT. , 1992, Medical physics.
[60] H L Kundel,et al. Nodule detection with and without a chest image. , 1985, Investigative radiology.