Home ›
Entities
› academia
› SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image
SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image
Research article (Computational and Mathematical Methods in Medicine, 2021) · cited 14× · AI/ML
SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image
Summary
SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image is a scholarly article[1].
Key Facts
SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
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). SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image. Retrieved May 24, 2026, from https://4ort.xyz/entity/serr-u-net-squeeze-and-excitation-residual-and-recurrent-block-based-u-net-for-automatic-vessel-segmentation-in-retinal-
MLA“SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/serr-u-net-squeeze-and-excitation-residual-and-recurrent-block-based-u-net-for-automatic-vessel-segmentation-in-retinal-.
BibTeX@misc{4ortxyz_serr-u-net-squeeze-and-excitation-residual-and-recurrent-block-based-u-net-for-automatic-vessel-segmentation-in-retinal-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image}}, year = {2026}, url = {https://4ort.xyz/entity/serr-u-net-squeeze-and-excitation-residual-and-recurrent-block-based-u-net-for-automatic-vessel-segmentation-in-retinal-}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image — https://4ort.xyz/entity/serr-u-net-squeeze-and-excitation-residual-and-recurrent-block-based-u-net-for-automatic-vessel-segmentation-in-retinal- (retrieved 2026-05-24)