Search Results for author:
Found 3 papers, 1 papers with code
A comprehensive survey on deep active learning in medical image analysis
Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets.
Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation
Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning.
TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning
In order to encourage different fusion tasks to promote each other and increase the generalizability of the trained network, we integrate the three self-supervised auxiliary tasks by randomly choosing one of them to destroy a natural image in model training.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
