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arXiv Computer Vision By Shengye Wang, Zonglin Wu, Liang Fan, Yule Xue, Haozhe Zhao

G^2RA-NET: Graph-based Cross-Slice Relation Modeling with Attention Gating for Medical Image Segmentation

Read the original on arXiv Computer Vision →

G^2RA‑Net is a medical image segmentation framework that models cross‑slice relations using a graph-based approach and refines segmentation with a cross‑slice attention gate. It represents each image slice as a graph node, propagates semantic context through graph message passing, and then selects relevant neighboring context to emphasize target anatomical regions. Experiments on brain MRI and abdominal CT datasets show that G^2RA‑Net improves segmentation accuracy and boundary quality compared to representative methods, with ablation studies confirming the effectiveness of its design.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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