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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In 2019, LaBonte et al. [686] used 3D Bayesian with VI for segmentation of material data (CT scans) on 2 datasets.
“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]
The Conclusion (page unavailable) says this paper uses a Bayesian neural network for phase imaging and phase retrieval, not segmentation. The Introduction describes two computational microscopy platforms and biological samples, not two CT datasets of material data. The cited paper is by Xue et al.; the passages do not support the LaBonte attribution or the claimed 3D variational-inference method. (LLM, conf=0.89)
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- 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 › Acoustics and Ultrasonics › Random lasers and scattering media
- Keywords
- phase imaging, uncertainty quantification, Bayesian convolutional neural network, coded illumination, confidence map, out-of-distribution prediction
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