Medical Imaging 2017: Computer-Aided Diagnosis
Proceedings Volume 10134 is from: Logo
11-16 February 2017
Orlando, Florida, United States
Front Matter: Volume 10134
Proc. SPIE 10134, Front Matter: Volume 10134, 1013401 (22 May 2017); doi: 10.1117/12.2277119
Proc. SPIE 10134, Segmentation of inner and outer bladder wall using deep-learning convolutional neural network in CT urography, 1013402 (3 March 2017); doi: 10.1117/12.2255528
Proc. SPIE 10134, Computer-aided detection of bladder masses in CT urography (CTU), 1013403 (3 March 2017); doi: 10.1117/12.2255668
Proc. SPIE 10134, Bladder cancer treatment response assessment using deep learning in CT with transfer learning, 1013404 (3 March 2017); doi: 10.1117/12.2254977
Proc. SPIE 10134, Convolutional neural network based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonance images, 1013405 (3 March 2017); doi: 10.1117/12.2254423
Proc. SPIE 10134, Quantification of oxygen changes in the placenta from BOLD MR image sequences, 1013406 (3 March 2017); doi: 10.1117/12.2254352
Lung I
Proc. SPIE 10134, Cascade of convolutional neural networks for lung texture classification: overcoming ontological overlapping, 1013407 (3 March 2017); doi: 10.1117/12.2255552
Proc. SPIE 10134, Identification of early-stage usual interstitial pneumonia from low-dose chest CT scans using fractional high-density lung distribution, 1013408 (3 March 2017); doi: 10.1117/12.2254126
Proc. SPIE 10134, 3D convolutional neural network for automatic detection of lung nodules in chest CT, 1013409 (3 March 2017); doi: 10.1117/12.2255795
Proc. SPIE 10134, Automatic detection of lung nodules: false positive reduction using convolution neural networks and handcrafted features, 101340A (3 March 2017); doi: 10.1117/12.2253995
Proc. SPIE 10134, The effects of slice thickness and radiation dose level variations on computer-aided diagnosis (CAD) nodule detection performance in pediatric chest CT scans, 101340B (27 March 2017); doi: 10.1117/12.2255000
Proc. SPIE 10134, Lung lesion detection in FDG-PET/CT with Gaussian process regression, 101340C (3 March 2017); doi: 10.1117/12.2255588
Colon and GI
Proc. SPIE 10134, Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging, 101340D (3 March 2017); doi: 10.1117/12.2253860
Proc. SPIE 10134, Deep multi-spectral ensemble learning for electronic cleansing in dual-energy CT colonography, 101340E (3 March 2017); doi: 10.1117/12.2254726
Proc. SPIE 10134, Fully convolutional neural networks for polyp segmentation in colonoscopy, 101340F (3 March 2017); doi: 10.1117/12.2254361
Proc. SPIE 10134, Deep ensemble learning of virtual endoluminal views for polyp detection in CT colonography, 101340G (3 March 2017); doi: 10.1117/12.2255606
Proc. SPIE 10134, Deep learning of contrast-coated serrated polyps for computer-aided detection in CT colonography, 101340H (3 March 2017); doi: 10.1117/12.2255634
Proc. SPIE 10134, A study of oral contrast coating on the surface of polyps: an implication for computer-aided detection and classification of polyps, 101340I (3 March 2017); doi: 10.1117/12.2254514
Proc. SPIE 10134, Computer assisted optical biopsy for colorectal polyps, 101340J (3 March 2017); doi: 10.1117/12.2254595
Proc. SPIE 10134, Automatic estimation of heart boundaries and cardiothoracic ratio from chest x-ray images, 101340K (3 March 2017); doi: 10.1117/12.2254136
Proc. SPIE 10134, Coronary artery calcification identification and labeling in low-dose chest CT images, 101340L (3 March 2017); doi: 10.1117/12.2254125
Proc. SPIE 10134, Coronary artery calcification (CAC) classification with deep convolutional neural networks, 101340M (3 March 2017); doi: 10.1117/12.2253974
Proc. SPIE 10134, Automatic extraction of disease-specific features from Doppler images, 101340N (3 March 2017); doi: 10.1117/12.2253956
Proc. SPIE 10134, Differentiation of pre-ablation and post-ablation late gadolinium-enhanced cardiac MRI scans of longstanding persistent atrial fibrillation patients, 101340O (3 March 2017); doi: 10.1117/12.2250910
Breast I
Proc. SPIE 10134, Radiomic modeling of BI-RADS density categories, 101340P (3 March 2017); doi: 10.1117/12.2255175
Proc. SPIE 10134, Neutrosophic segmentation of breast lesions for dedicated breast CT, 101340Q (3 March 2017); doi: 10.1117/12.2254128
Proc. SPIE 10134, Fully automated breast density assessment from low-dose chest CT, 101340R (3 March 2017); doi: 10.1117/12.2254151
Proc. SPIE 10134, Convolutional neural network approach for enhanced capture of breast parenchymal complexity patterns associated with breast cancer risk, 101340S (3 March 2017); doi: 10.1117/12.2254506
Proc. SPIE 10134, Identifying key radiogenomic associations between DCE-MRI and micro-RNA expressions for breast cancer, 101340T (3 March 2017); doi: 10.1117/12.2255512
Proc. SPIE 10134, Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning, 101340U (3 March 2017); doi: 10.1117/12.2255316
Liver and Abdomen
Proc. SPIE 10134, Superpixel-based classification of gastric chromoendoscopy images, 101340W (3 March 2017); doi: 10.1117/12.2254187
Proc. SPIE 10134, Quantification of CT images for the classification of high- and low-risk pancreatic cysts, 101340X (3 March 2017); doi: 10.1117/12.2255626
Proc. SPIE 10134, Automated liver elasticity calculation for 3D MRE, 101340Y (3 March 2017); doi: 10.1117/12.2254476
Proc. SPIE 10134, The effects of iterative reconstruction in CT on low-contrast liver lesion volumetry: a phantom study, 101340Z (3 March 2017); doi: 10.1117/12.2255743
Proc. SPIE 10134, Preoperative assessment of microvascular invasion in hepatocellular carcinoma, 1013410 (3 March 2017); doi: 10.1117/12.2255622
Musculoskeletal and Dermatology
Proc. SPIE 10134, Combination of lateral and PA view radiographs to study development of knee OA and associated pain, 1013411 (3 March 2017); doi: 10.1117/12.2254295
Proc. SPIE 10134, Characterizing cartilage microarchitecture on phase-contrast x-ray computed tomography using deep learning with convolutional neural networks, 1013412 (3 March 2017); doi: 10.1117/12.2254645
Proc. SPIE 10134, Classification of micro-CT images using 3D characterization of bone canal patterns in human osteogenesis imperfecta, 1013413 (3 March 2017); doi: 10.1117/12.2254421
Proc. SPIE 10134, Automated melanoma recognition in dermoscopic images based on extreme learning machine (ELM), 1013414 (3 March 2017); doi: 10.1117/12.2255576
Keynote and Reviewers' Choice
Proc. SPIE 10134, FDA's role in the innovation and evaluation of evolving CAD solutions (Conference Presentation), 1013415 (); doi: 10.1117/12.2257728
Proc. SPIE 10134, Organ detection in thorax abdomen CT using multi-label convolutional neural networks, 1013416 (3 March 2017); doi: 10.1117/12.2254349
Proc. SPIE 10134, Mammographic phenotypes of breast cancer risk driven by breast anatomy, 1013417 (3 March 2017); doi: 10.1117/12.2254630
Proc. SPIE 10134, Validation of an image registration and segmentation method to measure stent graft motion on ECG-gated CT using a physical dynamic stent graft model, 1013418 (3 March 2017); doi: 10.1117/12.2254262
Proc. SPIE 10134, Automatic detection and quantification of pulmonary arterio-venous malformations in hereditary hemorrhagic telangiectasia, 1013419 (3 March 2017); doi: 10.1117/12.2254754
Proc. SPIE 10134, Pelvic artery calcification detection on CT scans using convolutional neural networks, 101341A (3 March 2017); doi: 10.1117/12.2255247
Proc. SPIE 10134, Vessel segmentation in 4D arterial spin labeling magnetic resonance angiography images of the brain, 101341B (3 March 2017); doi: 10.1117/12.2254119
Proc. SPIE 10134, Computer-assisted adjuncts for aneurysmal morphologic assessment: toward more precise and accurate approaches, 101341C (3 March 2017); doi: 10.1117/12.2255553
Proc. SPIE 10134, Hessian-assisted supervoxel: structure-oriented voxel clustering and application to mediastinal lymph node detection from CT volumes, 101341D (3 March 2017); doi: 10.1117/12.2254782
Breast II
Proc. SPIE 10134, Conditional random field modelling of interactions between findings in mammography, 101341E (3 March 2017); doi: 10.1117/12.2254133
Proc. SPIE 10134, Deep learning and three-compartment breast imaging in breast cancer diagnosis, 101341F (3 March 2017); doi: 10.1117/12.2254516
Proc. SPIE 10134, Performance comparison of deep learning and segmentation-based radiomic methods in the task of distinguishing benign and malignant breast lesions on DCE-MRI, 101341G (3 March 2017); doi: 10.1117/12.2255582
Proc. SPIE 10134, Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches, 101341H (3 March 2017); doi: 10.1117/12.2249981
Proc. SPIE 10134, Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features, 101341I (3 March 2017); doi: 10.1117/12.2255731
Proc. SPIE 10134, Deep learning of symmetrical discrepancies for computer-aided detection of mammographic masses, 101341J (3 March 2017); doi: 10.1117/12.2254586
Proc. SPIE 10134, Estimation of retinal vessel caliber using model fitting and random forests, 101341K (3 March 2017); doi: 10.1117/12.2252025