Ai training
DocumentText Generation
Ai training
Ai training
2024 - Present
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Key Skills
Software
Appen
Labelbox
MercorMicro1
TelusOther
Scale AI
RemotasksTop Subject Matter
Medical Imaging AI/Healthcare
Legal Services & Contract Review
Regulatory Compliance & Risk Analysis
Top Data Types
Image
Text
DocumentTop Task Types
Data Collection
Bounding Box
Point Key Point
Text Generation
Question Answering
Text Summarization
Transcription
Prompt Response Writing SFT
Function Calling
Computer Programming Coding
RLHF
Freelancer Overview
Conditional Diffusion Model for Medical Image Generation. Brings 1+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include PyTorch. Education includes Doctor of Medicine, School of Medicine, Federal University o(2030). AI-training focus includes data types such as Medical and DICOM and labeling workflows including Data Collection.
Entry Level
Labeling Experience
Conditional Diffusion Model for Medical Image Generation
Data Collection
This project involved the creation of synthetic radiology images using a conditional diffusion model to augment existing datasets for AI training. The AI models were retrained on the enriched dataset to improve diagnostic robustness and clinical reliability. Data was labeled and validated to ensure accuracy and usefulness for downstream classifier development.
• Synthetic medical images were generated and curated for AI model training.
• Labeling and augmentation focused on critical radiological features for diagnostic enhancement.
• Diagnostic outcomes were tracked pre- and post-augmentation to assess impact.
• Project contributed to advancing responsible AI in clinical imaging.
This project involved the creation of synthetic radiology images using a conditional diffusion model to augment existing datasets for AI training. The AI models were retrained on the enriched dataset to improve diagnostic robustness and clinical reliability. Data was labeled and validated to ensure accuracy and usefulness for downstream classifier development. • Synthetic medical images were generated and curated for AI model training. • Labeling and augmentation focused on critical radiological features for diagnostic enhancement. • Diagnostic outcomes were tracked pre- and post-augmentation to assess impact. • Project contributed to advancing responsible AI in clinical imaging.
2024 - 2024
Education
S
School of Medicine, Federal University of [Your University Name]
Doctor of Medicine, Medicine
Doctor of Medicine
2023 - 2030
Work History
B
Biomedical Computing Lab
Undergraduate Research Assistant
N/A
2023 - 2023