Physical mechanisms governing generalization and hallucination in deep learning for imaging through scattering media.

Xuyu Zhang, Tianting Zhong, Haofan Huang, Dawei Zhang, Songlin Zhuang, Shensheng Han, Puxiang Lai, Honglin Liu

Abstract read

In one paragraph

Article in Nature communications, 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

8 authors.

Xuyu Zhang *Wangzhijiang Innovation Center for Laser, Aerospace Laser Technology and System Department, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, China.

Tianting Zhong *Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China.

Haofan Huang *Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China.

Dawei ZhangSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.ORCID http://orcid.org/0000-0002-0841-7826

Songlin ZhuangSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.

Shensheng HanWangzhijiang Innovation Center for Laser, Aerospace Laser Technology and System Department, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, China.

Puxiang LaiDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China. puxiang.lai@polyu.edu.hk.ORCID http://orcid.org/0000-0003-4811-2012

Honglin LiuWangzhijiang Innovation Center for Laser, Aerospace Laser Technology and System Department, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, China. hlliu4@hotmail.com.ORCID http://orcid.org/0000-0003-2473-9056

Funding

National Natural Science Foundation of China (National Science Foundation of China) 81930048

6 · The paper itself

Abstract

Deep learning has revolutionized computational imaging, yet its real-world deployment remains constrained by two critical challenges: poor generalization under dynamic conditions and the emergence of hallucinatory artifacts. By leveraging a physics-guided framework based on scattering media, a model system where controlled variations in light transmission matrices (

Identifiers

PMID42026070

DOI10.1038/s41467-026-72304-z

PMCPMC13315912

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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.