David Alonso-Caneiro
researcher
Facts
7 properties- Instance of
- human
- Occupation
- researcher
- Employer
- Queensland University of Technology (2011–present)[2]
- Educated at
- Queensland University of Technology (2007–2010)[3]
- Imported from
- wholeness_audit_2026-05-02
🏷️ Also known as
en David Alonso-Caneiro
🔖 Identifiers · 5
- ORCID iD
- 0000-0002-7754-6592
- OpenAlex ID
- A5002372452, A5122887027, A5128547721, A5135483011as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
- DBLP author ID
- 09/868
- Google Scholar author ID
- fhazMS4AAAAJ
- Scopus author ID
- 25225156600
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 24
A comparison of deep learning U-Net architectures for posterior segment OCT retinal layer segmentation,
A deep learning method for automatic segmentation of the bony orbit in MRI and CT images,
Application of Deep Learning Methods for Binarization of the Choroid in Optical Coherence Tomography Images,
Automatic choroidal segmentation in OCT images using supervised deep learning methods,
Automatic Detection of Cone Photoreceptors With Fully Convolutional Networks,
Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search,
Comparison of Choroidal Thickness Measurements Using Semiautomated and Manual Segmentation Methods,
Constructing Synthetic Chorio-Retinal Patches using Generative Adversarial Networks
+16 more
← Authored by 24
A comparison of deep learning U-Net architectures for posterior segment OCT retinal layer segmentation,
A deep learning method for automatic segmentation of the bony orbit in MRI and CT images,
Application of Deep Learning Methods for Binarization of the Choroid in Optical Coherence Tomography Images,
Automatic choroidal segmentation in OCT images using supervised deep learning methods,
Automatic Detection of Cone Photoreceptors With Fully Convolutional Networks,
Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search,
Comparison of Choroidal Thickness Measurements Using Semiautomated and Manual Segmentation Methods,
Constructing Synthetic Chorio-Retinal Patches using Generative Adversarial Networks
+16 more
Press Enter · cited answer in seconds