PROJECTS & RESEARCH

Overview

BioAnalytiX is developing an AI assistant for medical image analysis, focusing on radiology workflows. The platform automates the entire pipeline from DICOM ingest through preprocessing, model training, and clinical validation.

Key Features

• Automated DICOM Processing: Built robust pipelines for ingesting and preprocessing medical imaging data in DICOM format
• CT Analysis Models: Developed and trained baseline models for CT scan analysis with focus on diagnostic accuracy
• Experiment Tracking: Implemented comprehensive MLOps infrastructure for versioning, monitoring, and comparing model iterations
• Clinical Validation: Leading iterative feedback loops with radiologists to refine workflows and ensure clinical relevance

Technical Stack

Partnerships

Established strategic collaborations with the General Hospital of Larissa and the University of Thessaly for research validation and clinical pilot programs.

Impact

Working towards MVP deployment in radiology departments to assist clinicians with faster, more accurate diagnostic support while maintaining full clinical oversight.

Overview

Comprehensive research thesis supervised by Prof. Fotios Kokkoras, exploring advanced neural network architectures and optimization strategies to improve diagnostic accuracy across multiple medical imaging modalities.

Research Focus

• Architecture Exploration: Systematic evaluation of CNNs, Vision Transformers, and hybrid architectures for medical image classification tasks
• Optimization Strategies: Investigated advanced training techniques including transfer learning, data augmentation, and domain adaptation
• Multi-Modal Learning: Explored methods to leverage multiple imaging modalities (CT, MRI, X-ray) for improved diagnostic performance
• Clinical Validation: Validated models against clinician annotations to ensure alignment with diagnostic standards

Key Contributions

Developed a framework for comparing model architectures across diverse imaging tasks, identified optimal pre-training strategies for medical domains, and demonstrated the importance of explainability for clinical adoption.

Technical Stack

Supervisor

Prof. Fotios Kokkoras
University of Thessaly, Department of Digital Systems

Impact

This foundational research directly informed the technical approach taken at BioAnalytiX and contributed to the broader understanding of how to effectively apply modern AI techniques to medical imaging challenges.