AI Innovation
Pioneering AI Technology in Oncology
Our dedicated AI innovation lab develops novel algorithms and machine learning approaches that address the unique challenges of cancer research, diagnosis, and treatment optimization.
Core Innovation Areas
Our AI research team focuses on developing novel computational approaches that address the most challenging aspects of cancer diagnosis, treatment, and research.
Advanced Deep Learning
Designing specialized neural network architectures optimized for oncology data with sparse labels and complex biomarkers.
Multimodal Learning Systems
Developing frameworks that integrate diverse data types—imaging, genomics, clinical—to create comprehensive cancer analysis platforms.
Oncology-Specialized LLMs
Training large language models on oncology literature and clinical protocols to assist researchers and clinicians with specialized knowledge access.
Medical Vision Systems
Creating advanced computer vision models for analyzing pathology slides, radiology images, and other visual oncology data with superior accuracy.
Treatment Optimization RL
Applying reinforcement learning to optimize treatment planning, drug dosing, and clinical decision support systems.
Federated Learning Systems
Building privacy-preserving learning frameworks that enable collaborative model training across institutions without sharing sensitive patient data.
Current Projects
Our team is currently engaged in several cutting-edge projects that demonstrate the practical applications of our AI research.
OncoBiomarker AI
An advanced system for identifying novel biomarkers from multi-omics data, enabling earlier cancer detection and more precise treatment targeting.
Status: Research phase, preliminary model development
PathologyGPT
A specialized large language model trained on pathology reports and medical literature, designed to assist pathologists in diagnosis and research.
Status: Model training, initial validation with clinical partners
TreatmentRL
A reinforcement learning system that optimizes cancer treatment regimens based on patient-specific factors, treatment history, and predicted outcomes.
Status: Simulation environment development, algorithm design
FedOnco
A federated learning platform enabling hospitals and research institutions to collaboratively train AI models on oncology data while preserving patient privacy.
Status: Framework development, partner institution onboarding
Our Technology Stack
We leverage cutting-edge tools and frameworks to build our AI systems, ensuring performance, reliability, and scalability.
PyTorch
Deep learning model development with dynamic computation graphs
TensorFlow
Production-ready machine learning with comprehensive ecosystem
Hugging Face
State-of-the-art natural language processing models and tools
MONAI
Specialized framework for medical imaging AI development
Ray
Distributed computing for scalable AI training and serving
Azure ML
Cloud platform for end-to-end machine learning lifecycle
NVIDIA Frameworks
Hardware-optimized AI libraries for GPU acceleration
MLflow
Open source platform for managing the ML lifecycle