Final Multimodal_Biomedical_Image_Fusion_Research.pptx

Final Multimodal_Biomedical_Image_Fusion_Research

1. Generalizable and Explainable Deep Learning Models for Multi- Modal Biomedical Image Fusion and Diagnosis Presenter: Karthika Krishna Assistant Professor Department of Computer Science and Engineering Providence College of Engineering, Chengannur 1 / 12

2. Research Focus Multi-modal biomedical image fusion for enhanced diagnostic capabilities Development of generalizable deep learning architectures for healthcare applications Implementation of explainable AI techniques for clinical trust and adoption Bridging the gap between advanced AI models and practical clinical applications Research Vision Combining cutting-edge AI with medical expertise to transform diagnostic imaging Enhanced Diagnostics ↓ Explainable Models ↓ Clinical Application 2 / 12

3. Current Challenges in Medical Imaging Single modality limitations: Each imaging technique captures only specific aspects of anatomy/pathology Diagnostic uncertainty: Clinicians often need complementary information from multiple sources Information integration: Manual correlation of different imaging modalities is time-consuming and subjective Need for automated, reliable fusion techniques that preserve critical diagnostic information Modality Challenges MRI Soft tissue contrast Long acquisition time CT Bone structure Radiation exposure PET Metabolic activity Low resolution Fusion Complementary data Integration complexity 3 / 12

4. State- of- the-Art Review Recent Advances Deep learning for MRI, CT, and PET image analysis Automated segmentation and classification Multimodal processing frameworks Current Fusion Techniques Feature-level fusion approaches Decision-level integration methods Hybrid fusion architectures Model Evolution From traditional CNNs to advanced architectures Generative Adversarial Networks (GANs) Vision Transformers and attention mechanisms Limitations Poor generalization across datasets Limited interpretability of results Domain-specific optimization challenges 4 / 12

5. Identified Research Gaps Key Gaps Technical Limitations Clinical Integration Trust & Transparency Validation Methods Modality isolation: Current models typically focus on single-modality data Limited generalization: Models trained on specific datasets fail to perform well in new environments Black box problem: Lack of transparency in decision- making reduces clinical trust Integration challenges: Difficulty in combining complementary information while minimizing artifacts 5 / 12

6. Research Objectives Develop generalizable deep learning architectures for multi-modal medical image fusion Design explainable AI frameworks that provide insight into fusion and diagnostic decisions Create models that effectively combine complementary information from MRI, CT, and PET Validate the clinical utility of developed models through expert evaluation 6 / 12

7. Methodology Data Acquisition and Preprocessing Standardization across modalities ↓ Model Architecture Multi-branch CNN and transformer- based fusion networks ↓ Explainability Integration Grad-CAM and attention visualization techniques ↓ Validation Framework Quantitative metrics and clinical expert evaluation 7 / 12

8. Equipment and Software Requirements Hardware GPU-enabled systems (NVIDIA RTX series) High-performance computing cluster Storage infrastructure for large datasets Software Frameworks PyTorch/TensorFlow OpenCV for image processing MONAI for medical imaging Visualization Tools TensorBoard for training monitoring Grad-CAM toolkit for model interpretation 3D visualization libraries Datasets Public medical imaging repositories Collaborative clinical data Synthetic data for pre- training 8 / 12

9. Expected Research Outcomes Novel fusion architectures with improved generalization capabilities Transparent AI models with built-in explanation mechanisms Quantitative improvement in diagnostic accuracy compared to single-modality approaches Open-source implementation to facilitate adoption and further research 9 / 12

10. Conclusion and Impact Enhanced diagnostic capabilities through effective multi-modal fusion Increased clinical trust through explainable AI techniques Contribution to broader AI adoption in healthcare settings Potential for extension to other medical imaging applications "Building bridges between advanced AI technology and practical clinical application" 10 / 12

11. Thank You 11 / 12 Questions?