ReviewPhotoacoustics2021
Review of deep learning for photoacoustic imaging.
Review in Photoacoustics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 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
90 citing papers in PubMed, 167 citations in OpenAlex.
- Review
- Deep learning-driven quantitative spectroscopic photoacoustic imaging for segmentation and oxygen saturation estimation.Ultrasonics · 2026Article
- Listening to Immunotherapy - Ultrasound and Photoacoustic Imaging Strategies to Guide and Enhance Cancer Treatments.Molecular imaging and biology · 2026Review
- PIT-Net: Physics-informed transformer with differentiable diffusion constraints for quantitative photoacoustic tomography of deep tissues.Photoacoustics · 2026Article
- Nanomaterials for In Vivo Photoacoustic Sensing: Emerging Strategies and Future Outlook.ACS sensors · 2026Review
- Three-dimensional aperture-driven photoacoustic tomography (3D-ADPAT).Science advances · 2026Article
- [Research progress of photoacoustic imaging in diagnosis of limb lymphedema].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2026Review
- Physics-consistent denoising of full-field photoacoustic displacement maps using an adaptive residual attention network.Photoacoustics · 2026Article
- Photoacoustic Imaging for Women's Gynecological Health: Advances and Clinical Prospects.Bioengineering (Basel, Switzerland) · 2026Review
- Deep-learning-Assisted Photoacoustic and Ultrasound Evaluation for Pre-transplant Human Liver Graft Quality and Transplant Suitability.medRxiv : the preprint server for health sciences · 2026Article
- A Comparative Study on Signal Decomposition Techniques for Stimulated Raman Photoacoustic Microscopy.Journal of biophotonics · 2026Article
- Circular-scanning variance delay and sum algorithm for photoacoustic imaging.Biomedical optics express · 2026Article
- Attention-driven complementary information fusion network for sparse photoacoustic image reconstruction.Photoacoustics · 2026Article
- Advances in photoacoustic imaging reconstruction and quantitative analysis for biomedical applications.Visual computing for industry, biomedicine, and art · 2026Review
- Sub-Diffraction Photoacoustic Microscopy Enabled by a Novel Phase-Shifted Excitation Strategy: A Numerical Study.Sensors (Basel, Switzerland) · 2026Article
- Edge-Aware Short-Chain Diffusion Enables High-Fidelity Sparse-Sampling Optoacoustic Tomography.BME frontiers · 2026Article
- Photoacoustic imaging combined with robotics: a review of current works.Biomedical optics express · 2025Review
- Artifacts in photoacoustic imaging: Origins and mitigations.Photoacoustics · 2025Article
- Sparse scanning encoding and neural network decoding for compressed photoacoustic microscopy.Photoacoustics · 2025Article
- Wavelet-enhanced residual optimal transport for Mamba-based image restoration in photoacoustic tomography.Photoacoustics · 2025Article
30 more citing papers are in PubMed but not listed here.
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began to surpass other established mature models on some important benchmarks. Later, it was widely used in academia and industry. Ranging from image analysis to natural language processing, it fully exerted its magic and now become the state-of-the-art machine learning models. Deep neural networks have great potential in medical imaging technology, medical data analysis, medical diagnosis and other healthcare issues, and is promoted in both pre-clinical and even clinical stages. In this review, we performed an overview of some new developments and challenges in the application of machine learning to medical image analysis, with a special focus on deep learning in photoacoustic imaging. The aim of this review is threefold: (i) introducing deep learning with some important basics, (ii) reviewing recent works that apply deep learning in the entire ecological chain of photoacoustic imaging, from image reconstruction to disease diagnosis, (iii) providing some open source materials and other resources for researchers interested in applying deep learning to photoacoustic imaging.
Indexed as
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.