Research &
Insights
Advancing early cancer detection through multimodal AI, explainable algorithms, and privacy-preserving machine learning for precision oncology.
Platform Overview
What is SeleneX?
Early Detection & Management of Ovarian Cancer
An innovative AI-driven platform leveraging cutting-edge multimodal data fusion to detect cancer at its earliest, most treatable stages.
20%+
Detection Improvement
100%
Privacy-First Design
CT / MRI
Genomics
EHRs
Symptoms
Multimodal Data Fusion
Imaging, omics, EHRs, and patient-reported data
Proprietary Knowledge Graph
Connecting diverse data sources intelligently
AI Agent Active
Orchestrating progressive data analysis...
Generative AI Agents
Intelligent orchestration of diagnostic workflows
Breath VOCs
Non-invasive
Biomarkers
Urine analysis
Imaging
CT / MRI / US
Advanced
If needed
Progressive Data Escalation
Starting non-invasive, advancing only when necessary — reducing patient burden and healthcare costs
Privacy-First Design
Federated learning keeps data secure and compliant
Low Risk
Review
Why?
Explainable AI
Transparent decisions clinicians can trust
Simulating...
Digital Twins
Virtual patient models for treatment simulation
Research & Projects
Explore our cutting-edge research projects advancing early cancer detection and precision medicine.
Early Detection Enhancement
Multimodal AI for Stage I–II Ovarian Cancer Detection. Improved early detection rates by over 20% through advanced data fusion.
Privacy-Preserving Federated Learning
Building a secure, compliant AI platform for multi-institutional collaboration. Zero raw data leaves hospital premises.
Explainable AI for Clinicians
Transparent AI reasoning with natural-language explanations, attention heatmaps, and confidence scoring. 85%+ clinician usefulness.
Synthetic Data Generation
Using diffusion models and GANs to generate compliant synthetic data for rare cancer subtypes while maintaining privacy.
Digital Twins for Precision Medicine
Patient-specific virtual models for therapy forecasting, recurrence prediction, and in silico therapy testing. Validated across 500+ datasets.
Adaptive Workflow Optimization
RL-based surveys and generative AI agents that optimize data collection ordering, minimizing patient burden while capturing critical cues.
Multimodal Integration Architecture
Unified patient view through cross-modal data fusion. Building a proprietary knowledge graph and GNN system that surpasses state-of-the-art models. Validated on 10,000+ records with enhanced gender-sensitive detection framework.
Core Technologies
Our Technology Stack
Advanced capabilities powering the next generation of early cancer detection.
Multimodal AI Fusion
Unified patient view through cross-modal data fusion. Integrating imaging, genomics, EHRs, and patient-reported symptoms into a single intelligent system.
Graph Neural Networks
Proprietary knowledge graph connecting diverse data sources intelligently.
Federated Learning
Privacy-preserving AI that learns without centralizing sensitive patient data.
Explainable AI
Transparent reasoning with attention heatmaps and confidence scoring.
Synthetic Data
Privacy-compliant data generation for rare cancer subtypes using diffusion models.
Digital Twins
Patient-specific virtual models for therapy forecasting and in silico testing.
Reinforcement Learning
Adaptive diagnostic workflows that improve with every patient interaction.
Precision Genomics
AI-driven genomic analysis for personalized therapy selection and recurrence risk profiling.
Edge AI Deployment
Optimized model deployment for low-latency medical diagnostic hardware.
How We Work
A proven methodology for healthcare AI projects
Discovery & Assessment
Understanding clinical workflows, data infrastructure, and regulatory requirements.
Research & Proposal
Thorough research, literature review, and custom strategy aligned with clinical goals.
Development
Agile development prioritizing safety, explainability, and seamless PACS/EHR integration.
Validation
Rigorous validation including bias testing, explainability verification, and clinical validation.
Monitoring & Support
Ongoing model monitoring, performance tracking, and continuous improvement.
Technology Collaboration
Working alongside the world's most innovative technology companies to advance AI-powered diagnostic capabilities.
Google Health
Medical imaging AI and cloud infrastructure for scalable diagnostic solutions.
Microsoft Research
Azure healthcare infrastructure and AI research collaboration for enterprise deployments.
NVIDIA Healthcare
GPU acceleration and CUDA optimization for real-time medical image processing.
Amazon Web Services
HIPAA-compliant cloud infrastructure and serverless computing for global scale.
Global Research Network
A world-class team of researchers and clinicians spanning continents
"Our international team of published researchers and senior clinicians drives groundbreaking research in multimodal AI, federated learning, and explainable diagnostics — pushing the boundaries of what's possible in early cancer detection."