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Found 5 papers, 4 papers with code
MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer
To the best of our knowledge, our MAST is the first transformer model dedicated to video polyp segmentation.
DisControlFace: Disentangled Control for Personalized Facial Image Editing
Our DisControlNet can perform robust editing on any facial image through training on large-scale 2D in-the-wild portraits and also supports low-cost fine-tuning with few additional images to further learn diverse personalized priors of a specific person.
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts
Extensive experiments and analysis on five real multi-center medical image datasets demonstrate the superiority of FEAL over the state-of-the-art active learning methods in federated scenarios with domain shifts.
Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation
In this paper, we propose a multi-source DG method called Treasure in Distribution (TriD), which constructs an unprecedented search space to obtain the model with strong robustness by randomly sampling from a uniform distribution.
DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions
The nuclei segmentation in histology images is challenging in variable conditions (clinical wild), such as poor staining quality, stain variability, tissue variability, and conditions having higher morphological variability.
