Proceedings Volume 10950 is from: Logo
SPIE MEDICAL IMAGING
16-21 February 2019
San Diego, California, United States
Breast I
Proc. SPIE 10950, Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation, 1095002 (13 March 2019); doi: 10.1117/12.2512940
Proc. SPIE 10950, Detecting mammographically-occult cancer in women with dense breasts using deep convolutional neural network and Radon cumulative distribution transform, 1095003 (13 March 2019); doi: 10.1117/12.2512446
Proc. SPIE 10950, Reducing overfitting of a deep learning breast mass detection algorithm in mammography using synthetic images, 1095004 (13 March 2019); doi: 10.1117/12.2512604
Proc. SPIE 10950, Deep learning for identifying breast cancer malignancy and false recalls: a robustness study on training strategy, 1095005 (13 March 2019); doi: 10.1117/12.2512942
Proc. SPIE 10950, Evaluating deep learning techniques for dynamic contrast-enhanced MRI in the diagnosis of breast cancer, 1095006 (13 March 2019); doi: 10.1117/12.2512667
Brain
Proc. SPIE 10950, Registration based detection and quantification of intracranial aneurysm growth, 1095007 (13 March 2019); doi: 10.1117/12.2512781
Proc. SPIE 10950, Reliability of computer-aided diagnosis tools with multi-center MR datasets: impact of training protocol, 1095008 (13 March 2019); doi: 10.1117/12.2512819
Proc. SPIE 10950, Automatic multi-modality segmentation of gross tumor volume for head and neck cancer radiotherapy using 3D U-Net, 1095009 (13 March 2019); doi: 10.1117/12.2513229
Proc. SPIE 10950, Automatic strategy for extraction of anthropometric measurements for the diagnostic and evaluation of deformational plagiocephaly from infant’s head models, 109500A (13 March 2019); doi: 10.1117/12.2512782
Proc. SPIE 10950, Radiomics of the lesion habitat on pre-treatment MRI predicts response to chemo-radiation therapy in Glioblastoma, 109500B (13 March 2019); doi: 10.1117/12.2512907
Proc. SPIE 10950, Modeling normal brain asymmetry in MR images applied to anomaly detection without segmentation and data annotation, 109500C (13 March 2019); doi: 10.1117/12.2512873
Breast II
Proc. SPIE 10950, Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing, 109500D (13 March 2019); doi: 10.1117/12.2508358
Proc. SPIE 10950, Multiview mammographic mass detection based on a single shot detection system, 109500E (13 March 2019); doi: 10.1117/12.2513136
Proc. SPIE 10950, A deep learning method for volumetric breast density estimation from processed full field digital mammograms, 109500F (13 March 2019); doi: 10.1117/12.2512818
Proc. SPIE 10950, Breast density follow-up decision support system using deep convolutional models, 109500G (13 March 2019); doi: 10.1117/12.2513047
Proc. SPIE 10950, DCE-MRI based analysis of intratumor heterogeneity by decomposing method for prediction of HER2 status in breast cancer, 109500H (13 March 2019); doi: 10.1117/12.2513102
Breast III and Heart
Proc. SPIE 10950, Association of computer-aided detection results and breast cancer risk , 109500I (13 March 2019); doi: 10.1117/12.2512585
Proc. SPIE 10950, Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images, 109500J (13 March 2019); doi: 10.1117/12.2511718
Proc. SPIE 10950, Visual evidence for interpreting diagnostic decision of deep neural network in computer-aided diagnosis, 109500K (13 March 2019); doi: 10.1117/12.2512621
Proc. SPIE 10950, Automated measurement of fetal right-myocardial performance index from pulsed wave Doppler spectrum, 109500L (13 March 2019); doi: 10.1117/12.2512402
Proc. SPIE 10950, An ensemble of U-Net architecture variants for left atrial segmentation, 109500M (13 March 2019); doi: 10.1117/12.2512905
Proc. SPIE 10950, A deep learning approach to classify atherosclerosis using intracoronary optical coherence tomography , 109500N (13 March 2019); doi: 10.1117/12.2513078
Lung I
Proc. SPIE 10950, PHT-bot: a deep learning based system for automatic risk stratification of COPD patients based upon signs of pulmonary hypertension, 109500O (13 March 2019); doi: 10.1117/12.2512469
Proc. SPIE 10950, Identifying disease-free chest x-ray images with deep transfer learning, 109500P (13 March 2019); doi: 10.1117/12.2513164
Proc. SPIE 10950, Analysis of deep convolutional features for detection of lung nodules in computed tomography, 109500Q (13 March 2019); doi: 10.1117/12.2512208
Proc. SPIE 10950, A combination of intra- and peritumoral features on baseline CT scans is associated with overall survival in non-small cell lung cancer patients treated with immune checkpoint inhibitors: a multi-agent multi-site study, 109500R (13 March 2019); doi: 10.1117/12.2513001
Proc. SPIE 10950, Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs, 109500S (13 March 2019); doi: 10.1117/12.2512752
Abdomen
Proc. SPIE 10950, Artifact-driven sampling schemes for robust female pelvis CBCT segmentation using deep learning, 109500T (13 March 2019); doi: 10.1117/12.2512727
Proc. SPIE 10950, A probabilistic approach for interpretable deep learning in liver cancer diagnosis, 109500U (13 March 2019); doi: 10.1117/12.2512473
Proc. SPIE 10950, Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports, 109500V (13 March 2019); doi: 10.1117/12.2512886
Proc. SPIE 10950, Bladder cancer staging in CT urography: estimation and validation of decision thresholds for a radiomics-based decision support system, 109500W (13 March 2019); doi: 10.1117/12.2513566
Proc. SPIE 10950, Automatic MR kidney segmentation for autosomal dominant polycystic kidney disease, 109500X (13 March 2019); doi: 10.1117/12.2512372
Proc. SPIE 10950, 2D and 3D bladder segmentation using U-Net-based deep-learning , 109500Y (13 March 2019); doi: 10.1117/12.2511890
Multiorgan and Colon
Proc. SPIE 10950, Automatic anatomy partitioning of the torso region on CT images by using a deep convolutional network with majority voting, 109500Z (13 March 2019); doi: 10.1117/12.2512651
Proc. SPIE 10950, Automatic multi-organ segmentation in thorax CT images using U-Net-GAN , 1095010 (13 March 2019); doi: 10.1117/12.2512552
Proc. SPIE 10950, Polyp segmentation and classification using predicted depth from monocular endoscopy, 1095011 (15 March 2019); doi: 10.1117/12.2513117
Proc. SPIE 10950, Computer-aided classification of colorectal polyps using blue-light and linked-color imaging, 1095012 (13 March 2019); doi: 10.1117/12.2508223
Proc. SPIE 10950, Ensemble 3D residual network (E3D-ResNet) for reduction of false-positive polyp detections in CT colonography, 1095013 (18 March 2019); doi: 10.1117/12.2512173
Proc. SPIE 10950, A local geometrical metric-based model for polyp classification, 1095014 (14 March 2019); doi: 10.1117/12.2513056
Proc. SPIE 10950, Polyp-size classification with RGB-D features for colonoscopy, 1095015 (13 March 2019); doi: 10.1117/12.2513093
Lung II
Proc. SPIE 10950, Handling label noise through model confidence and uncertainty: application to chest radiograph classification, 1095016 (13 March 2019); doi: 10.1117/12.2514290
Proc. SPIE 10950, Classification of chest CT using case-level weak supervision, 1095017 (13 March 2019); doi: 10.1117/12.2513576
Proc. SPIE 10950, Deep adversarial one-class learning for normal and abnormal chest radiograph classification, 1095018 (13 March 2019); doi: 10.1117/12.2511787
Proc. SPIE 10950, Image biomarkers for quantitative analysis of idiopathic interstitial pneumonia, 1095019 (13 March 2019); doi: 10.1117/12.2511847
Radiomics I
Proc. SPIE 10950, Effect of diversity of patient population and acquisition systems on the use of radiomics and machine learning for classification of 2,397 breast lesions , 109501A (13 March 2019); doi: 10.1117/12.2512507
Proc. SPIE 10950, Radiogenomic characterization of response to chemo-radiation therapy in glioblastoma is associated with PI3K/AKT/mTOR and apoptosis signaling pathways, 109501B (13 March 2019); doi: 10.1117/12.2512258
Proc. SPIE 10950, Identifying optimal input using multilevel radiomics and nested cross-validation for predicting pulmonary function in lung cancer patients treated with radiotherapy, 109501C (13 March 2019); doi: 10.1117/12.2513083
Proc. SPIE 10950, Texture-based prostate cancer classification on MRI: how does inter-class size mismatch affect measured system performance? , 109501D (13 March 2019); doi: 10.1117/12.2513301