The paper reports a 0.71M-parameter encoder-decoder network that combines multi-scale convolutions, focal modulation attention, and spatial feature refinement, with reported dice scores of 86.44%, 87.88%, and 84.22% on DRIVE, STARE, and CHASE_DB.
An improved retinal vessel segmentation framework using frangi filter coupled with the probabilistic patch based denoiser,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
eess.IV 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
LVS-Net: A Lightweight Vessels Segmentation Network for Retinal Image Analysis
The paper reports a 0.71M-parameter encoder-decoder network that combines multi-scale convolutions, focal modulation attention, and spatial feature refinement, with reported dice scores of 86.44%, 87.88%, and 84.22% on DRIVE, STARE, and CHASE_DB.