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