Evidence map›Paper›PMID 38859913›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2022

Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation.

Yanwu Xu, Shaoan Xie, Maxwell Reynolds, Matthew Ragoza, Mingming Gong, Kayhan Batmanghelich

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentation.Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops · 2025
    Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yanwu XuDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA.
Shaoan XieDepartment of Philosophy, Carnegie Mellon University, Pittsburgh, USA.
Maxwell ReynoldsDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA.
Matthew RagozaDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA.
Mingming GongSchool of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia.
Kayhan BatmanghelichDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA.

Funding

Integrating Multi-Modal Data and Biomechanics in COPD: Toward Robust and Interpretable Biomarkers for Disease Subtyping and ProgressionR01HL141813 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Kayhan Batmanghelich · 2018 to 2026
$4.2M
Interpretable graphical models for large multi-modal COPD data (R01HL159805)R01HL159805 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BENOS, PANAGIOTIS V, SPIRTES, PETER · 2021 to 2024
$2.0M
NHLBI NIH HHS R01 HL141813NHLBI NIH HHS R01 HL159805
6 · The paper itself

Abstract

An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models. Domain Generalization (DG) aims to achieve this goal. However, most DG methods for segmentation require training data from multiple domains during training. We propose a novel adversarial domain generalization method for organ segmentation trained on data from a

Indexed as

Adversarial trainingMedical image segmentationMutual informationSingle domain generalization

Identifiers

PMID38859913
PMCPMC11164048

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.