Oyegbata O.

Key Skills

Software

Don't disclose

Top Subject Matter

Medical imaging (DICOM) and diagnostic radiology for AI/ML datasets

Legal Services & Contract Review

Regulatory Compliance & Risk Analysis

Top Data Types

Text

Document

Medical Dicom

Top Task Types

Segmentation

Bounding Box

Polygon

Classification

Freelancer Overview

Medical Radiographer/ CT SPECIALIST / Radiographic Data Analyst (Medical Imaging Annotation & QC) | Lead City University Teaching Hospital, . Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose. Education includes Bachelor of Science, Lead City University (2024). AI-training focus includes data types such as Medical, DICOM, and Medical DICOM and labeling workflows including Segmentation, Bounding Box, and Polygon.

Labeling Experience

Clinical Intern / Radiographic Data Analyst - Lead City University Teaching Hospital

Bounding BoxPolygon

As a Clinical Intern and Radiographic Data Analyst, you supported diagnostic imaging activities and contributed to maintaining reliable radiographic data for clinical and research use. You evaluated and annotated diagnostic scans while strictly following radiological protocols and physician specifications. You performed quality control checks on imaging datasets by verifying DICOM metadata and cross-referencing findings against clinical guidelines. • Evaluated and annotated 500+ X-ray, CT, and MRI scans • Conducted DICOM quality assurance to prevent data corruption • Identified anomalies, misalignments, and pathological markers • Maintained confidential digital records for multi-site research collaboration

2023 - 2024

Clinical Intern / Radiographic Data Analyst (Medical Imaging Annotation & QC) | Lead City University Teaching Hospital, Ibadan

Don't discloseSegmentation

Annotated 500+ diagnostic scans across X-ray, CT, and MRI using DICOM-compliant, protocol-adherent labeling workflows. Performed quality control to verify anatomical and pathological markers while ensuring accurate DICOM metadata labeling. Cross-referenced imaging findings against clinical guidelines to detect anomalies and structural misalignments. • Data annotation of anatomical structures and pathology-relevant regions • Quality assurance checks to prevent data corruption and labeling drift • Ground-truth validation using physician/radiological protocol specifications • Standardization of imaging interpretation workflows for AI-readiness

2023 - 2024

Education

Work History