Deep neural networks trained for segmentation are sensitive to brightness changes: preliminary results
A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography
Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features
Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images
Deep learning of 3D computed tomography (CT) images for organ segmentation using 2D multi-channel SegNet model
Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports
Breast tumor segmentation in DCE-MRI using fully convolutional networks with an application in radiogenomics
Improving classification with forced labeling of other related classes: application to prediction of upstaged ductal carcinoma in situ using mammographic features
Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: preliminary data
Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning
Breast mass detection in mammography and tomosynthesis via fully convolutional network-based heatmap regression
Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: a large scale evaluation
Radiogenomic analysis of lower grade glioma: a pilot multi-institutional study shows an association between quantitative image features and tumor genomics
Statistical aspects of radiogenomics: can radiogenomics models be used to aid prediction of outcomes in cancer patients?
Can BI-RADS features on mammography be used as a surrogate for expensive genomic testing in breast cancer patients?
Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?
Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features
Deep learning for segmentation of brain tumors: can we train with images from different institutions?
Predicting outcomes in glioblastoma patients using computerized analysis of tumor shape: preliminary data
Radiogenomics of glioblastoma: a pilot multi-institutional study to investigate a relationship between tumor shape features and tumor molecular subtype
Identification of error making patterns in lesion detection on digital breast tomosynthesis using computer-extracted image features
Incorporating breast tomosynthesis into radiology residency: Does trainee experience in breast imaging translate into improved performance with this new modality?
Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: preliminary experiments
Modeling error in assessment of mammographic image features for improved computer-aided mammography training: initial experience
Perception-driven IT-CADe analysis for the detection of masses in screening mammography: initial investigation
Relational representation for improved decisions with an information-theoretic CADe system: initial experience
A comparative study of database reduction methods for case-based computer-aided detection systems: preliminary results
Database decomposition of a knowledge-based CAD system in mammography: an ensemble approach to improve detection
Toward perceptually driven image retrieval in mammography: a pilot observer study to assess visual similarity of masses