Namrata P.

Key Skills

Software

CVAT

Supervisely

Label Studio

Labelbox

Roboflow

Top Subject Matter

Medical imaging (Histopathology, WSI, Tumor/Cell segmentation)

Industrial/computer vision (manufacturing defects, rust/corrosion, PCB anomaly)

Medical imaging (diagnostic/anemia-related region labeling)

Top Data Types

Image

Document

Text

Top Task Types

Segmentation

Bounding Box

Polygon

Object Detection

Entity (NER) Classification

Point/Key Point

Transcription

Text Generation

Freelancer Overview

AIML Engineer / Data Annotation Specialist — Medical Image Annotation. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include QuPath, ASAP, and CVAT. Education includes Bachelor of Technology, Dr. Babasaheb Ambedkar Technological University (DBATU) (2025) and Diploma, Maharashtra State Board of Technical Engineering (2022). AI-training focus includes data types such as Medical, DICOM, and Image and labeling workflows including Segmentation, Bounding Box, and Polygon.

Labeling Experience

AI/ML Engineer (Data Annotation Specialist) - Pixonate Lab

CVATImagePolygonSegmentation

You work as an AIML engineer and data annotation specialist focused on producing high-quality computer-vision training data. You handle medical and industrial imaging workflows that require accurate labeling standards and reliable export formats for downstream model training. The role requires strong command of Python-based tooling and segmentation/ROI methodologies using annotation platforms and validation practices. • Perform large-scale histopathology and WSI tissue annotation using QuPath and ASAP • Create tumor and cell-level polygon masks and ROI boundaries with GeoJSON-compatible outputs • Deliver semantic and instance segmentation datasets and maintain quality across large patch volumes • Support industrial OCR and defect/measurement-based labeling using CVAT and Label Studio

2025 - Present

AIML Engineer / Data Annotation Specialist — Medical Image Annotation

Segmentation

Medical Image Annotation Specialist responsible for large-scale histopathology and whole-slide image (WSI) labeling to support AI/ML model training pipelines. Created polygon masks, ROI boundaries, and pixel-wise labels for tumor, stroma, necrosis, nuclei, and related tissue classes with class-level consistency across datasets. Performed quality-focused dataset curation and exports in common training formats for downstream ML engineering teams.• Labeled Hamamatsu NDPI WSI and histopathology images using QuPath and ASAP.• Produced nuclei/cell-level annotations at sub-micron resolution and generated GeoJSON-compatible outputs.• Maintained annotation quality across 50,000+ patches for semantic and instance segmentation models.• Collaborated with AI/ML engineers to align label specifications with model architecture requirements.

2025 - Present

Medical Imaging Annotation Support - N/A

CVATImageBounding BoxSegmentation

You supported data preparation work for a multi-stage medical diagnostic imaging pipeline using object detection and segmentation. You performed QA-oriented organization and validation of labeled data to ensure clinically interpretable and leakage-safe datasets. The work required familiarity with annotation workflows and competency in computer-vision labeling for medical AI systems. • Annotate conjunctival and palmar region images using CVAT and Label Studio • Generate bounding box and semantic segmentation labels for YOLOv8, U-Net, and ViT-B/16 stages • Organize annotations in date-wise batches and run quality checks • Ensure compliance with medical AI dataset preparation standards

2025 - 2026

Non-Invasive Anemia Detection — Medical Imaging Annotation Support

CVATImageSegmentationBounding Box

Supported AI training data preparation for a three-stage diagnostic pipeline by annotating region images with bounding boxes and semantic segmentation labels. Organized annotation batches date-wise and performed QA reviews to ensure leakage-safe, clinically interpretable datasets. Contributed labeled data that supported YOLOv8, U-Net, and ViT-B/16 components for diagnostic model development.• Annotated conjunctival and palmar region images with precise bounding box and semantic segmentation labels.• Applied QA review processes to maintain labeling integrity and clinical interpretability.• Implemented leakage-safe dataset organization for medical AI standards compliance.• Provided training-ready labeled outputs for multi-stage model workflows.

2025 - 2026

Histopathology Tumor Segmentation & Cell Annotation Dataset

LabelboxImageSegmentation

No description provided.

2025 - 2025

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Education

Work History