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-# 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