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Polygon-mamba network improves retinal vessel segmentation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel hybrid CNN-Mamba network called Polygon-mamba for segmenting small retinal vessels, a task crucial for diagnosing eye diseases. The model incorporates a polygon scanning visual state space model (PS-VSS) to better preserve the connectivity of small vessels, addressing limitations of traditional horizontal-vertical scanning. Additionally, a space-frequency collaborative attention mechanism (SFCAM) is used to enhance feature extraction by integrating spatial and frequency domain information. Tested on three public datasets, Polygon-mamba achieved competitive performance with F1 scores around 0.828 and AUC values near 0.98. AI

IMPACT Introduces a new model architecture for medical image segmentation, potentially improving diagnostic accuracy for eye diseases.

RANK_REASON Publication of an academic paper on a novel computer vision model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Polygon-mamba network improves retinal vessel segmentation

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Publication of an academic paper on a novel computer vision model. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Juan Zhou ·

    Polygon-mamba: Retinal vessel segmentation using polygon scanning mamba and space-frequency collaborative attention

    Retinal vessel segmentation is crucial for diagnosis and assessment of ocular diseases. Notably, segmentation of small retinal vessels has been consistently recognized as a challenging and complex task. To tackle this challenge, we design a hybrid CNN-Mamba fusion network that in…

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