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Reliable deep-learning-based phase imaging with uncertainty quantification
Open accessAbstract
Emerging deep-learning (DL)-based techniques have significant potential to revolutionize biomedical imaging. However, one outstanding challenge is the lack of reliability assessment in the DL predictions, whose errors are commonly revealed only in hindsight. Here, we propose a new Bayesian convolutional neural network (BNN)-based framework that overcomes this issue by quantifying the uncertainty of DL predictions. Foremost, we show that BNN-predicted uncertainty maps provide surrogate estimates of the true error from the network model and measurement itself. The uncertainty maps characterize imperfections often unknown in real-world applications, such as noise, model error, incomplete training data, and out-of-distribution testing data. Quantifying this uncertainty provides a per-pixel estimate of the confidence level of the DL prediction as well as the quality of the model and data set. We demonstrate this framework in the application of large space-bandwidth product phase imaging using a physics-guided coded illumination scheme. From only five multiplexed illumination measurements, our BNN predicts gigapixel phase images in both static and dynamic biological samples with quantitative credibility assessment. Furthermore, we show that low-certainty regions can identify spatially and temporally rare biological phenomena. We believe our uncertainty learning framework is widely applicable to many DL-based biomedical imaging techniques for assessing the reliability of DL predictions.
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“LaBonte al. [686] | et | 2019 | 3D Bayesian | Segmentation | Material data (CT scans) | 2 | VI”
From A review of uncertainty quantification in deep learning: Techniques, applications and challenges (2021) · cites this paper as [686]
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Zhou, X., Yan, W., Peng, X., & Qu, J. (2026) · Laser & Photonics Review · doi:10.1002/lpor.71911
Metadata
- Authors
- Xue, Y., Cheng, S., Li, Y., & Tian, L.
- Affiliations
- Boston University (US)
- Corresponding author
- Tian, Lei
- Venue
- Optica
- Publisher
- Optica Publishing Group
- Published
- 2019-05-07
- Volume
- 6
- Issue
- 5
- Pages
- 618
- ISSN
- 2334-2536 (ISSN-L)
- PMC
- PMC8329751
- OpenAlex
- W2909560691
- Cited by
- 198
- References
- 48
- Citations by year
- 2026: 13 · 2025: 28 · 2024: 23 · 2023: 21 · 2022: 32 · 2021: 41 · 2020: 30 · 2019: 10
- FWCI
- 12.44
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-04-09)
- Open access
- gold
- doi.org
- Locations
- Optica (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- OpenBU (Boston University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1813848)
- National Institutes of Health (R21GM128020)
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Digital Holography and Microscopy
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Optical measurement and interference techniques
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Random lasers and scattering media
- Keywords
- phase imaging, uncertainty quantification, Bayesian convolutional neural network, coded illumination, confidence map, out-of-distribution prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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