CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation
Summary
CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation is a scholarly article[1].
Key Facts
CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation's instance of is recorded as scholarly article[2].
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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). CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation. Retrieved May 24, 2026, from https://4ort.xyz/entity/ct-male-pelvic-organ-segmentation-using-fully-convolutional-networks-with-boundary-sensitive-representation
MLA“CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/ct-male-pelvic-organ-segmentation-using-fully-convolutional-networks-with-boundary-sensitive-representation.
BibTeX@misc{4ortxyz_ct-male-pelvic-organ-segmentation-using-fully-convolutional-networks-with-boundary-sensitive-representation_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation}}, year = {2026}, url = {https://4ort.xyz/entity/ct-male-pelvic-organ-segmentation-using-fully-convolutional-networks-with-boundary-sensitive-representation}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation — https://4ort.xyz/entity/ct-male-pelvic-organ-segmentation-using-fully-convolutional-networks-with-boundary-sensitive-representation (retrieved 2026-05-24)
