Evidence map›Paper›PMID 39831950›Full record

ArticleMedical & biological engineering & computing2025

C

Wei Zhou, Xuekun Yang, Jianhang Ji, Yugen Yi

Abstract read
PubMed Publisher
In one paragraph

Article in Medical & biological engineering & computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Wei ZhouCollege of Computer Science, Shenyang Aerospace University, Shenyang, 110136, China. zhouweineu@outlook.com.ORCID http://orcid.org/0000-0002-5931-3197
Xuekun YangCollege of Computer Science, Shenyang Aerospace University, Shenyang, 110136, China.
Jianhang JiFaculty of Data Science, City University of Macau, Macau, China.
Yugen YiSchool of Software, Jiangxi Normal University, Nanchang, 330022, China.

Funding

Fundamental Research Funds for the Universities of Liaoning Province No.LJ212410143028Jiangxi Province Key Subject Academic and Technical Leader Funding Project No. 20212BCJ23017National Natural Science Foundation of China Nos. 62062040, 62102270, 62041702Natural Science Foundation of Liaoning Province No. 2023-MS-246Outstanding Youth Project of Jiangxi Natural Science Foundation No. 20212ACB212003
6 · The paper itself

Abstract

Source-free domain adaptation (SFDA) has become crucial in medical image analysis, enabling the adaptation of source models across diverse datasets without labeled target domain images. Self-training, a popular SFDA approach, iteratively refines self-generated pseudo-labels using unlabeled target domain data to adapt a pre-trained model from the source domain. However, it often faces model instability due to incorrect pseudo-label accumulation and foreground-background class imbalance. This paper presents a pioneering SFDA framework, named cascaded network-guided class-balanced multi-prototype auxiliary learning (C

Indexed as

Image Processing, Computer-AssistedMachine LearningNeural Networks, ComputerAlgorithmsHumansMedical image segmentationModel stabilityReliable pseudo-labelSource-free domain adaptation

Identifiers

PMID39831950

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.