Search Results for author:
Found 13 papers, 7 papers with code
Spatiotemporal Representation Learning for Short and Long Medical Image Time Series
Moreover, tracking longer term developments that occur over months or years in evolving processes, such as age-related macular degeneration (AMD), is essential for accurate prognosis.
Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers
Direct image-to-graph transformation is a challenging task that solves object detection and relationship prediction in a single model.
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines
In this paper, we propose a method to propagate uncertainty through cascades of deep learning models in medical imaging pipelines.
3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images
In this work, we propose a novel method to segment the 3D peripancreatic arteries solely from one annotated 2D projection per training image with depth supervision.
A skeletonization algorithm for gradient-based optimization
Finally, we demonstrate the utility of our algorithm by integrating it with two medical image processing applications that use gradient-based optimization: deep-learning-based blood vessel segmentation, and multimodal registration of the mandible in computed tomography and magnetic resonance images.
Unlocking the Diagnostic Potential of ECG through Knowledge Transfer from Cardiac MRI
In a qualitative analysis, we demonstrate that our learned ECG embeddings incorporate information from CMR image regions of interest.
Metrics to Quantify Global Consistency in Synthetic Medical Images
In this work, we introduce two metrics that can measure the global consistency of synthetic images on a per-image basis.
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations
To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data.
Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data
Medical datasets and especially biobanks, often contain extensive tabular data with rich clinical information in addition to images.
Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration
Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly.
Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias
Recent studies suggest that early stages of diabetic retinopathy (DR) can be diagnosed by monitoring vascular changes in the deep vascular complex.
Metadata-enhanced contrastive learning from retinal optical coherence tomography images
This is exacerbated in longitudinal image datasets that repeatedly image the same patient cohort to monitor their disease progression over time.
Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs
Encouraged by our method's competitive quantitative and superior qualitative performance, we believe that it constitutes a versatile tool to advance the quantitative analysis of OCTA images.
