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V
Vishvajit

Vishvajit

Aspiring AI Engineer | Deep Learning | Data-Driven Mindset

India flagSurat, India
$10.00/hrExpertClickworkerCloudfactoryDataloop

Key Skills

Software

ClickworkerClickworker
CloudFactoryCloudFactory
DataloopDataloop
Data Annotation TechData Annotation Tech
ProdigyProdigy
SlothSloth

Top Subject Matter

Healthcare
Finance
e-Commerce

Top Data Types

TextText
ImageImage
DocumentDocument

Top Task Types

ClassificationClassification
Text SummarizationText Summarization
SegmentationSegmentation
TranscriptionTranscription
Entity (NER) ClassificationEntity (NER) Classification
Bounding BoxBounding Box

Freelancer Overview

Medical Imaging Data Annotation and Classification (Brain Tumor MRI). Brings 20+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Doctor of Philosophy, Uka Tarsadia University (2024) and Master of Technology, Gujarat Technological University (2013). AI-training focus includes data types such as Medical, DICOM, and Text and labeling workflows including Classification and Text Summarization.

ExpertEnglishHindiMarathi

Labeling Experience

Medical Imaging Data Annotation and Classification (Brain Tumor MRI)

Classification
Developed a robust brain tumor classification system using labeled MRI images and deep learning techniques. Conducted extensive data augmentation and labeling of medical scans to facilitate supervised learning for tumor identification. Leveraged manual annotation and review processes to ensure high-quality data for model training. • Annotated and categorized brain tumor MRI images by type and region. • Utilized labeling for training Convolutional Neural Networks (CNNs) and InceptionV3 architecture. • Created labeled datasets to improve healthcare diagnostic models. • Implemented rigorous data validation and correction protocols.

Developed a robust brain tumor classification system using labeled MRI images and deep learning techniques. Conducted extensive data augmentation and labeling of medical scans to facilitate supervised learning for tumor identification. Leveraged manual annotation and review processes to ensure high-quality data for model training. • Annotated and categorized brain tumor MRI images by type and region. • Utilized labeling for training Convolutional Neural Networks (CNNs) and InceptionV3 architecture. • Created labeled datasets to improve healthcare diagnostic models. • Implemented rigorous data validation and correction protocols.

2025 - 2025

Devanagari OCR Data Annotation

ImageClassification
Led the development of a Devanagari Optical Character Recognition (OCR) system requiring manual annotation of character images. Labeled thousands of handwritten and printed characters for model training and validation. Used custom dataset creation and annotation guidelines for quality control. • Classified and segmented Devanagari script images for supervised OCR learning. • Used labeled data to train and evaluate deep learning models. • Built a diverse, labeled corpus for robust character recognition. • Ensured annotation accuracy through iterative review cycles.

Led the development of a Devanagari Optical Character Recognition (OCR) system requiring manual annotation of character images. Labeled thousands of handwritten and printed characters for model training and validation. Used custom dataset creation and annotation guidelines for quality control. • Classified and segmented Devanagari script images for supervised OCR learning. • Used labeled data to train and evaluate deep learning models. • Built a diverse, labeled corpus for robust character recognition. • Ensured annotation accuracy through iterative review cycles.

2020 - 2020

Research Article Summarization Data Labeling (deepMINE)

TextText Summarization
Developed an NLP-based automated literature mining system for biomedical research articles during the COVID-19 pandemic. Labeled and summarized over 29,000 scientific research articles to create training datasets for supervised summarization models. Employed annotation quality checks and manual validation for accuracy and completeness of summaries. • Generated ground-truth summaries from biomedical papers for deep learning. • Used annotated text pairs for training summarization algorithms. • Curated a large, labeled corpus for rapid information retrieval tasks. • Addressed quality and consistency in labeled scientific datasets.

Developed an NLP-based automated literature mining system for biomedical research articles during the COVID-19 pandemic. Labeled and summarized over 29,000 scientific research articles to create training datasets for supervised summarization models. Employed annotation quality checks and manual validation for accuracy and completeness of summaries. • Generated ground-truth summaries from biomedical papers for deep learning. • Used annotated text pairs for training summarization algorithms. • Curated a large, labeled corpus for rapid information retrieval tasks. • Addressed quality and consistency in labeled scientific datasets.

2020 - 2020

Image Caption Annotation for Captioning System

ImageClassification
Worked on an image captioning system using deep learning, requiring image labeling and annotation of captions. Annotated diverse image datasets with contextually relevant captions for sequence model training. Focused on quality, variety, and relevance of labeled image-caption pairs. • Generated labeled image-caption pairs for model fine-tuning. • Assessed annotation consistency and fluency through quality audits. • Used annotated samples for transformer-based decoder training. • Provided feedback loops for iterative improvement of labeled data.

Worked on an image captioning system using deep learning, requiring image labeling and annotation of captions. Annotated diverse image datasets with contextually relevant captions for sequence model training. Focused on quality, variety, and relevance of labeled image-caption pairs. • Generated labeled image-caption pairs for model fine-tuning. • Assessed annotation consistency and fluency through quality audits. • Used annotated samples for transformer-based decoder training. • Provided feedback loops for iterative improvement of labeled data.

2017 - 2017

Education

U

Uka Tarsadia University

Doctor of Philosophy, Computer Engineering

Doctor of Philosophy
2020 - 2024
G

Gujarat Technological University

Master of Technology, Computer Engineering

Master of Technology
2011 - 2013

Work History

A

Asha M. Tarsadia Institute of Computer Science and Technology

Director

Bardoli
2024 - Present
U

Uka Tarsadia University

Web and E-Gov Cell Incharge

Bardoli
2024 - Present