ArticleOptica2019
Reliable deep-learning-based phase imaging with uncertainty quantification.
Article in Optica, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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.
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.
Who cites it
45 citing papers in PubMed.
- Deep learning-driven quantitative spectroscopic photoacoustic imaging for segmentation and oxygen saturation estimation.Ultrasonics · 2026Article
- Robust agricultural pest detection under occlusion and environmental variations via AGIIN-MAF training and SAODL adaptation.Frontiers in plant science · 2026Article
- A neural network for long-term super-resolution imaging of live cells with reliable confidence quantification.Nature biotechnology · 2026Article
- Fourier ptychographic enhancement of iterative pathways: autonomous 3D momentum coordination in hybrid ML-PIE architectures.Biomedical optics express · 2025Article
- Fast Fourier ptychographic microscopy based on annular illumination and parallel acquisition.Biomedical optics express · 2025Article
- Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems.Transactions on machine learning research · 2025Article
- Deep-learning-enabled spatial frequency domain imaging of the spatiotemporal dynamics of skin physiology.Journal of biomedical optics · 2025Article
- Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo.Biosensors & bioelectronics · 2025Review
- A guidance to intelligent metamaterials and metamaterials intelligence.Nature communications · 2025Review
- Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Prediction.Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2025Article
- Joint segmentation and image reconstruction with error prediction in photoacoustic imaging using deep learning.Photoacoustics · 2024Article
- Neural network-based processing and reconstruction of compromised biophotonic image data.Light, science & applications · 2024Review
- Review
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- TIE-GANs: single-shot quantitative phase imaging using transport of intensity equation with integration of GANs.Journal of biomedical optics · 2024Article
- Fast Fourier ptychographic quantitative phase microscopy for in vitro label-free imaging.Biomedical optics express · 2024Article
- Roadmap on Label-Free Super-Resolution Imaging.Laser & photonics reviews · 2023Article
- Artificial intelligence-enabled quantitative phase imaging methods for life sciences.Nature methods · 2023Review
- A Deep Learning-Based Framework for Uncertainty Quantification in Medical Imaging Using the DropWeak Technique: An Empirical Study with Baresnet.Diagnostics (Basel, Switzerland) · 2023Article
- Optical ptychography for biomedical imaging: recent progress and future directions [Invited].Biomedical optics express · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
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.