Evidence map›Paper›PMID 41658490›Full record

ArticleInnovation (Cambridge (Mass.))2026

Self-adaptive fine-tuning of deep learning super-resolution microscopy for artifact suppression in live-cell imaging.

Tianjie Yang, Jia He, Xian'ao Zhao, Congmin Ren, Zhuoli Ding, Lu Wang, Hanqing Zhao, Ling Chu, Siyuan Luo, Chaojing Shi and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Innovation (Cambridge (Mass.)), 2026. 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

14 authors.

Tianjie YangState Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Jia HeSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
Xian'ao ZhaoState Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Congmin RenState Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Zhuoli DingState Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Lu WangKey Laboratory of Smart Drug Delivery, Ministry of Education, School of Pharmacy, Fudan University, Shanghai 201203, China.
Hanqing ZhaoKey Laboratory of Smart Drug Delivery, Ministry of Education, School of Pharmacy, Fudan University, Shanghai 201203, China.
Ling ChuSchool of Pharmaceutical Sciences, MOE Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Tsinghua University, Beijing 100084, China.
Siyuan LuoSchool of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China.
Chaojing ShiState Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Lusheng GuState Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Tao XuState Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Ge YangSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
Wei JiState Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In deep learning super-resolution microscopy, concerns exist about the generation of artifacts, and methods for artifact suppression are lacking. We developed a self-adaptive fine-tuning method that dynamically adjusts the parameters of the models to minimize the loss function, which includes direct quantification of artifacts from live-cell imaging. Integrating self-adaptive fine-tuning with super-resolution models enables significant artifact reduction in the visualization of nanoscale organelle interactions at high spatial-temporal resolution.

Indexed as

adaptive imagingartifactdeep learninglive-cell imagingmicroscopysuper-resolution

Identifiers

PMID41658490
PMCPMC12881779

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.