https://github.com/cv-inside/ks-lectures
Science Score: 36.0%
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○CITATION.cff file
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✓codemeta.json file
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✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org, sciencedirect.com, wiley.com, ieee.org -
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○Scientific vocabulary similarity
Low similarity (5.2%) to scientific vocabulary
Last synced: 6 months ago
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- Host: GitHub
- Owner: CV-INSIDE
- Default Branch: main
- Size: 12.7 KB
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Created over 1 year ago
· Last pushed over 1 year ago
Metadata Files
Readme
README.md
ks-lectures
To incur in the ks project, the following readings:
- On the in vivo recognition of kidney stones using machine learning [Paper] 🌟
- Assessing deep learning methods for the identification of kidney stones in endoscopic images [Paper]
- Classification of Stones According to Michel Daudon: A Narrative Review [Paper] 🌟
- Evaluation and understanding of automated urinary stone recognition methods [Paper] 🌟
- Boosting kidney stone identification in endoscopic images using two-step transfer learning [Paper] [Repo]
- Improved kidney stone recognition through attention and multi-view feature fusion strategies [Paper]
- On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification [Paper]
- Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning [Paper]
- Evaluating the plausibility of synthetic images for improving automated endoscopic stone recognition [Paper]
- A metric learning approach for endoscopic kidney stone identification [Paper]
🌟 Mandatory paper
I will soon add the respective repositories
Last updated by Francisco: 11 Sept '24
Owner
- Name: CV-INSIDE
- Login: CV-INSIDE
- Kind: organization
- Location: Mexico
Computer Vision for Image aNalysiS & bIomeDical Engineering. Tecnologico de Monterrey. School of Engineering and Sciences