ArticleScientific reports2020
Autoencoder based blind source separation for photoacoustic resolution enhancement.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed, 12 citations in OpenAlex.
- Article
- Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder.Journal of grid computing · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Photoacoustics is a promising technique for in-depth imaging of biological tissues. However, the lateral resolution of photoacoustic imaging is limited by size of the optical excitation spot, and therefore by light diffraction and scattering. Several super-resolution approaches, among which methods based on localization of labels and particles, have been suggested, presenting promising but limited solutions. This work demonstrates a novel concept for extended-resolution imaging based on separation and localization of multiple sub-pixel absorbers, each characterized by a distinct acoustic response. Sparse autoencoder algorithm is used to blindly decompose the acoustic signal into its various sources and resolve sub-pixel features. This method can be used independently or as a combination with other super-resolution techniques to gain further resolution enhancement and may also be extended to other imaging schemes. In this paper, the general idea is presented in details and experimentally demonstrated.
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.