187 papers with code • 28 benchmarks • 26 datasets
Medical image segmentation is the task of segmenting objects of interest in a medical image.
( Image credit: IVD-Net )
The large-scale OCTA dataset is available at https://doi. org/10. 5281/zenodo. 5111975, https://doi. org/10. 5281/zenodo. 5111972.
Domain Adaptation (DA) methods are widely used in medical image segmentation tasks to tackle the problem of differently distributed train (source) and test (target) data.
We construct a modified version of U-Net shape network with additional encoder and decoder and compute a saliency map in each bottom-up stream prediction module and propagate to the next prediction module.
Ranked #1 on Medical Image Segmentation on ETIS-LARIBPOLYPDB
The application of PTs to FL in medical imaging and the trade-offs between privacy guarantees and model utility, the ramifications on training performance and the susceptibility of the final models to attacks have not yet been conclusively investigated.
In this paper, we present a cooperative framework for training image segmentation models and a latent space augmentation method for generating hard examples.
We show that these synthetic data generation pipelines can be used as an alternative to bypass privacy concerns and as an alternative way to produce artificial segmentation datasets with corresponding ground truth masks to avoid the tedious medical data annotation process.
The proposed network has two appealing characteristics: 1) The memory-augmented network offers the ability to quickly encode past segmentation information, which will be retrieved for the segmentation of other slices; 2) The quality assessment module enables the model to directly estimate the qualities of segmentation predictions, which allows an active learning paradigm where users preferentially label the lowest-quality slice for multi-round refinement.