LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation
Quick Answer
LightVesselNet is an ultra-lightweight neural network with only 75K parameters, achieving competitive retinal blood vessel segmentation performance.
Quick Take
LightVesselNet is an ultra-lightweight neural network with only 75K parameters, achieving competitive retinal blood vessel segmentation performance. Tested on five datasets, it shows sensitivity scores up to 0.8640 and Dice coefficients up to 0.8649, making it ideal for low-resource clinical settings.
Key Points
- LightVesselNet employs a compact encoder-decoder architecture with attention mechanisms.
- Achieves sensitivity scores ranging from 0.8096 to 0.8640 across multiple datasets.
- Demonstrates improved efficiency compared to state-of-the-art models in retinal segmentation.
- Includes a dedicated edge residual connection for preserving fine vessel details.
- Proven generalization capability through cross-dataset evaluation.
Article Content
From source RSS / original summaryarXiv:2606. 05354v1 Announce Type: new Abstract: Retinal blood vessel segmentation plays a vital role in the early detection of diabetic retinopathy and glaucoma. While recent deep learning models have achieved great segmentation accuracy, they typically require heavy computational resources, making real-world deployment on edge devices difficult. In this paper, we propose LightVesselNet, an efficient neural network designed for retinal vessel segmentation in a resource-constrained environment.
Despite containing only 75K parameters, LightVesselNet performs competitively with much larger models. The network employs a compact encoder decoder architecture enhanced with channel and spatial attention mechanisms, a multi-scale feature aggregation module at the bottleneck, and a subpixel upsampling strategy in the decoder. A dedicated edge residual connection preserves fine vessel detail throughout decoding.
Extensive experiments on five publicly available datasets: DRIVE, STARE, CHASEDB1, FIVES, and HRF, yield sensitivity scores of 0. 8189, 0. 8499, 0. 8640, 0. 8634, 0. 8096, and Dice coefficients of 0. 8070, 0. 8072, 0. 8181, 0. 8649, and 0. 7686, respectively. LightVesselNet shows improved efficiency (Performance vs Parameter or GFlops) compared to State-of-the-Art models. Cross-dataset evaluation confirms the model's generalisation capability.
Overall, LightVesselNet is a strong candidate for deployment in low-resource clinical settings and mobile screening tools.
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