ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels
Summary
ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels is a scholarly article[1].
Key Facts
ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels's instance of is recorded as scholarly article[2].
References
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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). ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels. Retrieved May 24, 2026, from https://4ort.xyz/entity/resdac-net-a-novel-pancreas-segmentation-model-utilizing-residual-double-asymmetric-spatial-kernels
MLA“ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/resdac-net-a-novel-pancreas-segmentation-model-utilizing-residual-double-asymmetric-spatial-kernels.
BibTeX@misc{4ortxyz_resdac-net-a-novel-pancreas-segmentation-model-utilizing-residual-double-asymmetric-spatial-kernels_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels}}, year = {2026}, url = {https://4ort.xyz/entity/resdac-net-a-novel-pancreas-segmentation-model-utilizing-residual-double-asymmetric-spatial-kernels}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels — https://4ort.xyz/entity/resdac-net-a-novel-pancreas-segmentation-model-utilizing-residual-double-asymmetric-spatial-kernels (retrieved 2026-05-24)
