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Found 5 papers, 4 papers with code
Prototype as Query for Few Shot Semantic Segmentation
In this way, the spatial details can be better captured and the semantic features of target class in the query image can be focused.
Ranked #5 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images
DASC-Net consists of a novel attention and feature domain enhanced domain adaptation model (AFD-DA) to solve the domain shifts and a self-correction learning process to refine segmentation results.
Free-form tumor synthesis in computed tomography images via richer generative adversarial network
The network is composed of a new richer convolutional feature enhanced dilated-gated generator (RicherDG) and a hybrid loss function.
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans
Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes.
DUNet: A deformable network for retinal vessel segmentation
Results show that more detailed vessels are extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0. 9697/0. 9722/0. 9724 and AUC of 0. 9856/0. 9868/0. 9863 on DRIVE, STARE and CHASE_DB1 respectively.
Ranked #5 on
Retinal Vessel Segmentation
on STARE
