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Found 7 papers, 0 papers with code
A deep learning algorithm for reducing false positives in screening mammography
This work lays the foundation for semi-autonomous breast cancer screening systems that could benefit patients and healthcare systems by reducing false positives, unnecessary procedures, patient anxiety, and expenses.
A Hypersensitive Breast Cancer Detector
In this work, we leverage a large set of FFDM images with loose bounding boxes of mammographically significant findings to train a deep learning detector with extreme sensitivity.
A Multi-site Study of a Breast Density Deep Learning Model for Full-field Digital Mammography Images and Synthetic Mammography Images
Conclusion: A BI-RADS breast density DL model demonstrated strong performance on FFDM and SM images from two institutions without training on SM images and improved using few SM images.
Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis
In particular, we use average histogram matching (HM) and DL fine-tuning methods to generalize a FFDM model to the 2D maximum intensity projection (MIP) of DBT images.
Probabilistic Graphical Modeling approach to dynamic PET direct parametric map estimation and image reconstruction
In this work, we introduce a new probabilistic modeling strategy based on the key assumption that activity time course would be subject to uncertainty even if the parameters of the underlying dynamic process were known.
DeepSPINE: Automated Lumbar Vertebral Segmentation, Disc-level Designation, and Spinal Stenosis Grading Using Deep Learning
The high prevalence of spinal stenosis results in a large volume of MRI imaging, yet interpretation can be time-consuming with high inter-reader variability even among the most specialized radiologists.
Kinetic Compressive Sensing
We evaluated the proposed algorithm on a simulated dynamic phantom: a bias/variance study confirmed how direct estimates can improve the quality of parametric maps over a post-reconstruction fitting, and showed how the novel sparsity prior can further reduce their variance, without affecting bias.
