Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment
Proceedings Volume 10952 is from: Logo
16-21 February 2019
San Diego, California, United States
Image Perception
Proc. SPIE 10952, Does the strength of the gist signal predict the difficulty of breast cancer detection in usual presentation and reporting mechanisms?, 1095203 (4 March 2019); doi: 10.1117/12.2513151
Proc. SPIE 10952, Oculomotor behaviour of radiologists reading digital breast tomosynthesis (DBT) , 1095204 (4 March 2019); doi: 10.1117/12.2513602
Model Observers I
Proc. SPIE 10952, Automatic strategy for CHO channel reduction in x-ray angiography systems, 1095205 (4 March 2019); doi: 10.1117/12.2513609
Proc. SPIE 10952, Template models for forced-localization tasks, 1095206 (4 March 2019); doi: 10.1117/12.2512302
Proc. SPIE 10952, Autoencoder embedding of task-specific information, 1095207 (4 March 2019); doi: 10.1117/12.2513120
Proc. SPIE 10952, Learning the Hotelling observer for SKE detection tasks by use of supervised learning methods, 1095208 (4 March 2019); doi: 10.1117/12.2512607
Proc. SPIE 10952, Learning the ideal observer for joint detection and localization tasks by use of convolutional neural networks, 1095209 (4 March 2019); doi: 10.1117/12.2513016
Model Observers II
Proc. SPIE 10952, Laguerre-Gauss and sparse difference-of-Gaussians observer models for signal detection using constrained reconstruction in magnetic resonance imaging, 109520A (4 March 2019); doi: 10.1117/12.2512813
Proc. SPIE 10952, Tests of projection and reconstruction domain equivalence for a feature-driven model observer, 109520B (5 March 2019); doi: 10.1117/12.2512900
Proc. SPIE 10952, New difference of Gaussian channel-sets for the channelized Hotelling observer?, 109520C (4 March 2019); doi: 10.1117/12.2512054
Proc. SPIE 10952, A foveated channelized Hotelling search model predicts dissociations in human performance in 2D and 3D images, 109520D (4 March 2019); doi: 10.1117/12.2511777
Proc. SPIE 10952, Using transfer learning for a deep learning model observer, 109520E (4 March 2019); doi: 10.1117/12.2511750
Technology Impact and Assessment
Proc. SPIE 10952, Estimating latent reader-performance variability using the Obuchowski-Rockette method, 109520F (4 March 2019); doi: 10.1117/12.2513106
Proc. SPIE 10952, Adaptive sample size re-estimation in MRMC studies, 109520G (4 March 2019); doi: 10.1117/12.2513646
Proc. SPIE 10952, Radiation therapy induced-erythema: comparison of spectroscopic diffuse reflectance measurements and visual assessment, 109520H (4 March 2019); doi: 10.1117/12.2506306
Proc. SPIE 10952, Impact of patient photos on detection accuracy, decision confidence, and eye-tracking parameters in chest and abdomen images with tubes and lines, 109520I (4 March 2019); doi: 10.1117/12.2511755
Proc. SPIE 10952, Is there a safety-net effect with computer-aided detection (CAD)?, 109520J (4 March 2019); doi: 10.1117/12.2512720
Deep Learning Applications
Proc. SPIE 10952, Correlation between a deep-learning-based model observer and human observer for a realistic lung nodule localization task in chest CT, 109520K (4 March 2019); doi: 10.1117/12.2513451
Proc. SPIE 10952, Implementation of an ideal observer model using convolutional neural network for breast CT images, 109520L (4 March 2019); doi: 10.1117/12.2512131
Proc. SPIE 10952, Learning stochastic object model from noisy imaging measurements using AmbientGANs, 109520M (4 March 2019); doi: 10.1117/12.2512633
Proc. SPIE 10952, BI-RADS density categorization using deep neural networks, 109520N (4 March 2019); doi: 10.1117/12.2513185
Proc. SPIE 10952, Mammographic breast density classification using a deep neural network: assessment on the basis of inter-observer variability, 109520O (4 March 2019); doi: 10.1117/12.2513420
Observer Performance
Proc. SPIE 10952, Development of methods to evaluate probability of reviewer’s assessment bias in blinded independent central review (BICR) imaging studies, 109520P (4 March 2019); doi: 10.1117/12.2512603
Proc. SPIE 10952, Reader Disagreement Index: a better measure of overall review quality monitoring in an oncology trial compared to adjudication rate, 109520Q (4 March 2019); doi: 10.1117/12.2512611
Proc. SPIE 10952, A 2-AFC study to validate artificially inserted microcalcification clusters in digital mammography, 109520R (4 March 2019); doi: 10.1117/12.2513031
Proc. SPIE 10952, The relationship between breast screening readers’ real-life performance and their associated performance on the PERFORMS scheme (Conference Presentation), 109520S (15 March 2019); doi: 10.1117/12.2512474
Proc. SPIE 10952, Blinding of the second reader in mammography screening: impact on behaviour and cancer detection, 109520T (4 March 2019); doi: 10.1117/12.2512090
Observer Performance in Breast Imaging
Proc. SPIE 10952, An observer study to assess the detection of calcification clusters using 2D mammography, digital breast tomosynthesis, and synthetic 2D imaging, 109520U (4 March 2019); doi: 10.1117/12.2506895
Proc. SPIE 10952, 2D single-slice vs. 3D viewing of simulated tomosynthesis images of a small-scale breast tissue model, 109520V (4 March 2019); doi: 10.1117/12.2512053
Proc. SPIE 10952, Changes in breast density, 109520W (4 March 2019); doi: 10.1117/12.2512252
Proc. SPIE 10952, Assessment of a quantitative mammographic imaging marker for breast cancer risk prediction, 109520X (4 March 2019); doi: 10.1117/12.2512802
Poster Session
Proc. SPIE 10952, Comparing senior residents performance to radiologists in lung cancer detection, 109520Y (4 March 2019); doi: 10.1117/12.2512755
Proc. SPIE 10952, Data transformations for variance stabilization in the statistical assessment of quantitative imaging biomarkers, 109520Z (4 March 2019); doi: 10.1117/12.2507295
Proc. SPIE 10952, A case study regarding clinical performance evaluation method of medical device software for approval, 1095210 (4 March 2019); doi: 10.1117/12.2511936
Proc. SPIE 10952, In-vitro and in-vivo comparison of radiation dose estimates between state-of-the-art interventional fluoroscopy systems, 1095211 (4 March 2019); doi: 10.1117/12.2512920
Proc. SPIE 10952, Prostate Imaging Self-assessment and Mentoring (PRISM): a prototype self-assessment scheme, 1095212 (4 March 2019); doi: 10.1117/12.2511960
Proc. SPIE 10952, Deep residual-network-based quality assessment for SD-OCT retinal images: preliminary study, 1095214 (4 March 2019); doi: 10.1117/12.2513607
Proc. SPIE 10952, A statistical analysis of oral tagging in CT colonography and its impact on flat polyp detection and characterization, 1095215 (4 March 2019); doi: 10.1117/12.2513148
Proc. SPIE 10952, Missed cancer and visual search of mammograms: what feature based machine-learning can tell us that deep-convolution learning cannot, 1095216 (4 March 2019); doi: 10.1117/12.2512539
Back to Top