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Found 7 papers, 2 papers with code
Unified Cross-Modal Image Synthesis with Hierarchical Mixture of Product-of-Experts
We propose a deep mixture of multimodal hierarchical variational auto-encoders called MMHVAE that synthesizes missing images from observed images in different modalities.
Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound
To disambiguate ultrasound imaging and adapt to the neurosurgeon's surgical objective, a patient-specific real-time network is trained using synthetic ultrasound data generated by simulating virtual iUS sweep acquisitions in pre-operative MR data.
Unimodal Cyclic Regularization for Training Multimodal Image Registration Networks
The loss function of an unsupervised multimodal image registration framework has two terms, i. e., a metric for similarity measure and regularization.
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time.
Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images
We propose and demonstrate machine learning algorithms to assess the severity of pulmonary edema in chest x-ray images of congestive heart failure patients.
Keypoint Transfer for Fast Whole-Body Segmentation
We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images.
Identification of Recurrent Patterns in the Activation of Brain Networks
Identifying patterns from the neuroimaging recordings of brain activity related to the unobservable psychological or mental state of an individual can be treated as a unsupervised pattern recognition problem.
