Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). LA-Net: layer attention network for 3D-to-2D retinal vessel segmentation in OCTA images. Retrieved May 24, 2026, from https://4ort.xyz/entity/la-net-layer-attention-network-for-3d-to-2d-retinal-vessel-segmentation-in-octa-images
MLA“LA-Net: layer attention network for 3D-to-2D retinal vessel segmentation in OCTA images.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/la-net-layer-attention-network-for-3d-to-2d-retinal-vessel-segmentation-in-octa-images.
BibTeX@misc{4ortxyz_la-net-layer-attention-network-for-3d-to-2d-retinal-vessel-segmentation-in-octa-images_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{LA-Net: layer attention network for 3D-to-2D retinal vessel segmentation in OCTA images}}, year = {2026}, url = {https://4ort.xyz/entity/la-net-layer-attention-network-for-3d-to-2d-retinal-vessel-segmentation-in-octa-images}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): LA-Net: layer attention network for 3D-to-2D retinal vessel segmentation in OCTA images — https://4ort.xyz/entity/la-net-layer-attention-network-for-3d-to-2d-retinal-vessel-segmentation-in-octa-images (retrieved 2026-05-24)
