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-# Detailed Publications and Research Contributions 
-
-## BioFusionNet (2024)
-**Full Title**: "BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion"
-
-**Journal**: IEEE Journal of Biomedical and Health Informatics
-
-**Key Contributions**:
-- Novel multimodal fusion architecture combining histopathology, genomics, and clinical data
-- Attention-based feature selection for interpretability
-- Superior performance compared to existing methods
-- Clinical validation on large patient cohorts
-
-**Technical Details**:
-- Uses ResNet-based feature extraction for histopathology images
-- Implements cross-attention mechanisms for data fusion
-- Employs survival analysis with Cox proportional hazards
-- Achieves C-index of 0.78 on validation datasets
-
-**Impact**: This work provides clinicians with a comprehensive tool for patient risk assessment, enabling personalized treatment planning.
-<!-- This is code for this paper -->
-**GitHub**: [raktim-mondol/BioFusionNet](https://github.com/raktim-mondol/BioFusionNet)
-
-## hist2RNA (2023)
-**Full Title**: "hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images"
-
-**Journal**: Cancers
-
-**Key Contributions**:
-- Direct prediction of gene expression from tissue images
-- Efficient architecture suitable for clinical deployment
-- Identification of morphology-gene expression relationships
-- Validation across multiple cancer datasets
-
-**Technical Details**:
-- Custom CNN architecture optimized for gene expression prediction
-- Multi-task learning framework
-- Attention mechanisms for spatial feature importance
-- Correlation analysis with known biological pathways
-
-**Impact**: Enables gene expression profiling without expensive molecular assays, making personalized medicine more accessible.
-<!-- This is code for this paper -->
-**GitHub**: [raktim-mondol/hist2RNA](https://github.com/raktim-mondol/hist2RNA)
-
-## AFExNet (2021)
-**Full Title**: "AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-types and Extracting Biologically Relevant Genes"
-
-**Journal**: IEEE/ACM Transactions on Computational Biology and Bioinformatics
-
-**Key Contributions**:
-- Adversarial training for robust feature learning
-- Automatic biomarker discovery
-- Cancer subtype classification
-- Biologically interpretable features
-
-**Technical Details**:
-- Adversarial autoencoder architecture
-- Gene selection based on reconstruction importance
-- Validation on TCGA datasets
-- Pathway enrichment analysis
-
-**Impact**: Provides insights into cancer biology while achieving high classification accuracy.
-<!-- This is code for this paper -->
-**GitHub**: [raktim-mondol/breast-cancer-sub-types](https://github.com/raktim-mondol/breast-cancer-sub-types)
-
-## Ongoing Research
-
-### Multimodal Foundation Models
-- Developing foundation models for medical imaging
-- Pre-training on large-scale medical datasets
-- Transfer learning for rare diseases
-
-### Ongoing Research
-- Large Language Models (LLMs)  
-- Retrieval-Augmented Generation (RAG)  
-- Fine-tuning and domain adaptation  
-
-
-### AI Ethics in Healthcare
-- Bias detection and mitigation
-- Fairness in medical AI
-- Regulatory compliance frameworks