knowledge_base/research_details.md

# Detailed Research Information

## PhD Research: Deep Learning Based Prognosis and Explainability for Breast Cancer

### Research Objectives
1. Develop novel deep learning architectures for breast cancer survival prediction
2. Create explainable AI models that clinicians can trust and understand
3. Integrate multimodal data (histopathology images, genomics, clinical data)
4. Build treatment recommendation systems based on patient-specific factors

### Key Innovations
- **BioFusionNet**: A multimodal fusion network that combines histopathology images with genomic and clinical data for survival risk stratification
- **hist2RNA**: An efficient architecture that predicts gene expression directly from histopathology images
- **AFExNet**: An adversarial autoencoder for cancer subtype classification and biomarker discovery

### Technical Approach
- Utilizes attention mechanisms for interpretability
- Employs transfer learning from pre-trained vision models
- Implements novel fusion strategies for multimodal data
- Uses adversarial training for robust feature learning

### Clinical Impact
The research aims to provide clinicians with:
- More accurate prognosis predictions
- Personalized treatment recommendations
- Explainable AI decisions for clinical trust
- Cost-effective diagnostic tools

## Current Projects

### Large Language Models for Healthcare
- Fine-tuning LLMs for medical text analysis
- Developing RAG systems for clinical decision support
- Creating conversational AI for patient education

### Multimodal AI Systems
- Vision-language models for medical imaging
- Cross-modal retrieval systems
- Multimodal fusion architectures

### Explainable AI
- Attention visualization techniques
- Counterfactual explanations
- Feature importance analysis
- Clinical decision support systems