Verified claim · AI-ML · 100% confidence
U-Net introduced in: Ronneberger, Fischer, Brox 2015 — biomedical image segmentation.
Last verified 2026-05-16 · Methodology veritas-v0.1 · 4f19829aa2036770
Structured fields
- Subject
- U-Net
- Predicate
introduced_in- Object
- Ronneberger, Fischer, Brox 2015 — biomedical image segmentation
- Confidence
- 100%
- Tags
- u-net · ronneberger · image-segmentation · diffusion-backbone · foundational · 2015 · introduced_in
Sources (2)
[1] preprint · arXiv (Ronneberger, Fischer, Brox / University of Freiburg) · 2015-05-18
U-Net: Convolutional Networks for Biomedical Image Segmentation“We present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization.”
[2] official blog · University of Freiburg · 2015-05-18
U-Net — official project page
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curl https://sourcescore.org/api/v1/claims/4f19829aa2036770.jsonJavaScript / TypeScript
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// "U-Net introduced in: Ronneberger, Fischer, Brox 2015 — biomedical image segmentation."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/4f19829aa2036770.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "U-Net introduced in: Ronneberger, Fischer, Brox 2015 — biomedical image segmentation."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_u_net_fact() -> dict:
"""Fetch the verified SourceScore claim for U-Net."""
r = httpx.get("https://sourcescore.org/api/v1/claims/4f19829aa2036770.json")
return r.json()