Unsupervised domain adaptation in biomedical images segmentation with guided diffusion generative prior

Authors

Publication Details

Journal
SPIRE - Sciences Po Institutional REpository
Published
2026-01-01
DOI
https://doi.org/10.1109/tip.2026.3678763
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Unsupervised domain adaptation in biomedical images segmentation with guided diffusion generative prior

Benoît Naegel, Nicolas Passat, Étienne Baudrier, Alexandre Stenger
SPIRE - Sciences Po Institutional REpository
Generative Adversarial Networks and Image Synthesis
article

Unsupervised domain adaptation in biomedical images segmentation with guided diffusion generative prior

Benoît Naegel, Nicolas Passat, Étienne Baudrier, Alexandre Stenger
article en

Abstract

No abstract available for this paper.
SPIRE - Sciences Po Institutional REpository
Openalex Percentile: Top 4%
Generative Adversarial Networks and Image Synthesis
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Unsupervised domain adaptation in biomedical images segmentation with guided diffusion generative prior — Benoît Naegel, Nicolas Passat, et al. · SPIRE - Sciences Po Institutional REpository (2026) | TGRS Research Map | TGRS