Andrii B.

Key Skills

Software

Other

Top Subject Matter

Medical imaging – gastrointestinal CT/MRI annotation for AI model training

MRI reconstruction and denoising with deep learning

Legal Services & Contract Review

Top Data Types

Image

Text

Document

Top Task Types

Classification

Segmentation

Freelancer Overview

CT/MRI Annotation Specialist – Binariks (NDA Project). Brings 16+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Doctor of Medicine, Danylo Halytsky Lviv National Medical University (2010) and Internship in Radiology, Danylo Halytsky Lviv National Medical University (2012). AI-training focus includes data types such as Medical and DICOM and labeling workflows including Classification and Segmentation.

Labeling Experience

AI / MRI Reconstruction Research Project – Independent R&D

OtherSegmentation

I am independently developing deep learning models for MRI reconstruction and denoising using real-world medical datasets. This project involves annotating undersampled k-space MRI data and creating ground truth references for AI training. My responsibilities include building preprocessing pipelines, conducting model experiments, and evaluating output quality. • Designed end-to-end annotation workflows, from DICOM import to model validation. • Applied PyTorch and NumPy for AI model training and dataset labeling. • Focused on improving MRI image quality and reducing artifacts for clinical utility. • Applied variational network architectures for accelerated MRI reconstruction.

2025 - Present

CT/MRI Annotation Specialist – Binariks (NDA Project)

Classification

I annotated CT and MRI datasets focusing on gastrointestinal structures for AI model training. My work ensured clinical accuracy and supported dataset preparation for machine learning pipelines. Tasks included labeling duodenum, small bowel, and colon images in collaboration with an international team. • Contributed to dataset quality assurance, emphasizing clinically relevant features. • Utilized internal tools and adhered to NDA requirements for sensitive medical data. • Collaborated with AI engineers and health tech professionals. • Supported integration of labeled datasets into machine learning workflows.

2025 - 2026

Education

Work History