Adianez Oramas - Digital Pathology Lead – Data Labeling and Validation

Key Skills

Software

Internal/Proprietary Tooling

Top Subject Matter

Digital Pathology

Oncologic Pathology

Histopathology Domain Expertise

Top Data Types

Image

Text

Document

Top Task Types

Diagnosis

Classification

Question Answering

Data Collection

Evaluation Rating

Text Generation

Text Summarization

Object Detection

Prompt Response Writing SFT

RLHF

Red Teaming

Entity Ner Classification

Freelancer Overview

Digital Pathology Lead – Data Labeling and Validation. Over 14 years of experience in anatomical pathology with extensive work in diagnostic classification, structured case review, and quality assurance of medical data. Experienced in validating and standardizing histopathological diagnoses for research datasets and clinical studies, ensuring consistency and accuracy in diagnostic labeling workflows. Strong background in collaborative research environments and medical data curation for structured analysis. Education includes a Master of Science in Oncologic Pathology (CEU Cardenal Herrera University, 2023) and Medical Specialty in Anatomical Pathology (Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán – UNAM, 2013).

IntermediateEnglishSpanishGerman

Labeling Experience

Digital Pathology Lead – Data Labeling and Validation

Diagnosis

Led the histopathological diagnosis process using whole slide imaging (WSI) in digital pathology workflows. Contributed high-quality diagnostic labels and validated medical imaging data sets to support AI-driven diagnostic solutions. Ensured datasets were consistently annotated to high clinical standards for accuracy and reproducibility. • Routine digital slide interpretation and pattern recognition for data labeling • Validated annotated images for AI model development in digital pathology • Structured diagnostic assessments supporting medical data integrity • Generated labeled datasets for use in AI-powered medical imaging research

2013 - Present

Head of Pathology – Data Validation for AI Training

Diagnosis

Directed diagnostic quality assurance workflows, producing structured validation labels for histopathology images within ISO 15189-compliant systems. Supervised and ensured accuracy of labeled data sets for clinicopathological correlation and AI-driven medical model applications. Led clinical validation efforts to guarantee data integrity and utility for downstream AI training. • Implemented and checked structured labeling standards in clinical data • Oversaw multidisciplinary quality review for labeled pathology slides • Coordinated between clinical and machine learning teams for annotation validation • Enhanced reproducibility of labeled datasets for AI systems training

2018 - 2025

Collaborative Study Contributor – Medical Data Annotation and AI Validation

Diagnosis

Participated in international collaborative studies standardizing structured medical data for AI-driven pathology systems. Engaged in the creation and validation of labeled datasets for cross-institutional research and e-learning case discussions. Focused on enhancing dataset consistency for use in machine learning development in diagnostic pathology applications. • Standardized annotation of histopathologic patterns in digital slides • Ensured consistency of medical dataset labels across institutions • Contributed to structured data frameworks for AI pathology research • Enhanced training data quality through rigorous validation protocols

2022 - 2023

Education

C

CEU Cardenal Herrera University

Master of Science, Oncologic Pathology

Master of Science

2022 - 2023

I

Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán – National Autonomous University of Mexico

Specialty in Anatomical Pathology, Anatomical Pathology

Specialty in Anatomical Pathology

2010 - 2013

Work History

A

ANA PAT Cancún

Digital Pathology Specialist

Cancún

2013 - Present

M

Medical University of Graz

Visiting Researcher

Graz

2025 - 2025