Shiv Saidha
Researcher / author (OpenAlex)
Facts
2 properties- Affiliation
- Johns Hopkins University, Johns Hopkins Medicine, Institute of Neuroimmunology of the Slovak Academy of Sciencesas 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
🔖 Identifiers · 2
- ORCID iD
- 0000-0001-6387-0714
- OpenAlex ID
- A5052025260as 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
📚 Sources · 1
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 22
Analysis of Agreement of Retinal-Layer Thickness Measures Derived from the Segmentation of Horizontal and Vertical Spectralis OCT Macular Scans,
APOSTEL 2.0 Recommendations for Reporting Quantitative Optical Coherence Tomography Studies,
Applying an Open-Source Segmentation Algorithm to Different OCT Devices in Multiple Sclerosis Patients and Healthy Controls: Implications for Clinical Trials,
DeepHarmony: A deep learning approach to contrast harmonization across scanner changes,
Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT,
Disentangled Representation Learning for OCTA Vessel Segmentation With Limited Training Data,
Fully Convolutional Boundary Regression for Retina OCT Segmentation,
Image Artifacts in Optical Coherence Tomography Angiography Among Patients With Multiple Sclerosis
+14 more
← Authored by 22
Analysis of Agreement of Retinal-Layer Thickness Measures Derived from the Segmentation of Horizontal and Vertical Spectralis OCT Macular Scans,
APOSTEL 2.0 Recommendations for Reporting Quantitative Optical Coherence Tomography Studies,
Applying an Open-Source Segmentation Algorithm to Different OCT Devices in Multiple Sclerosis Patients and Healthy Controls: Implications for Clinical Trials,
DeepHarmony: A deep learning approach to contrast harmonization across scanner changes,
Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT,
Disentangled Representation Learning for OCTA Vessel Segmentation With Limited Training Data,
Fully Convolutional Boundary Regression for Retina OCT Segmentation,
Image Artifacts in Optical Coherence Tomography Angiography Among Patients With Multiple Sclerosis
+14 more
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Summary
Shiv Saidha is a human[1].