Exciting advancements in biomedical research and healthcare are emerging through the fusion of AI and multimodal data analysis. The “Multimodal Learning in Biomedical Applications” (MLBA) special track aims to bring together the latest research and developments in this dynamic field.
We invite contributions that push the boundaries of knowledge and application in multimodal learning, enhancing our ability to address complex biomedical challenges and improve patient outcomes.

Topics

Topics include but are not limited to:

1. Fundamentals of Multimodal Learning in Biomedicine: Exploring the basic principles and theories underpinning multimodal learning in the biomedical context.

2. Data Fusion Techniques: Techniques and strategies for integrating diverse biomedical data types (e.g., imaging, genomic, clinical data) in multimodal learning models.

3. Model Interpretability: Approaches to make multimodal learning models more interpretable and understandable in a biomedical setting.

4. Resilient Models: Developing systems that operate effectively in challenging, noisy, incomplete, and uncertain real-world biomedical settings.

5. Personalized Medicine: Using multimodal data to develop personalized treatment plans and predictive models in healthcare.

6. Clinical Decision Support Systems: The role of multimodal learning in enhancing clinical decision-making processes.

7. Medical Imaging and Diagnostics: Application of multimodal learning in medical imaging, diagnostics, and pathology.

8. Ethical and Legal Considerations: Addressing the ethical, legal, and social implications of applying multimodal learning in biomedicine.

9. Big Data: Challenges and solutions in handling large-scale multimodal biomedical data.

10. Patient Monitoring and Wearable Technologies: Integration of data from wearable devices and patient monitoring systems using multimodal learning approaches.

11. AI and Drug Discovery: Leveraging multimodal learning in drug discovery and development processes.

12. Predictive Analytics in Healthcare: Using multimodal data for predictive analytics and forecasting in healthcare settings.

13. Performance Evaluation: Methods and metrics for assessing the performance of multimodal learning models in biomedicine.

14. Human-Centered Design: Exploring models that co-evolve with human input at both individual and collective levels in biomedical contexts.

15. Adaptive Learning Systems: Investigating systems that can perceive, learn, and act in dynamically changing biomedical environments.

16. Human-Machine Interaction: Promoting effective interaction and collaboration between humans and machines in the context of multimodal learning for biomedicine.

17. Edge/Exascale Computing: Operating at micro-level on the edge and macro-level on the cloud in the development of multimodal learning systems for biomedicine.

18. Green-Aware Learning: Incorporating environmental considerations by design in the development of multimodal learning systems for biomedicine.

19. Robotics: Investigating the integration of robotics with multimodal learning systems for enhanced diagnostic, surgical, and therapeutic applications in healthcare.

20. Rehabilitation Technologies: Exploring the use of multimodal learning to innovate rehabilitation technologies, focusing on personalized treatment plans, assistive devices, and improving patient outcomes in rehabilitation settings.

Important Dates

The special track will take place in parallel with the general conference track. Submission deadlines are as follows:

• Paper submission deadline: 17-04-2024

• Notification of acceptance: From 29-04-2024

• Camera-ready due: 17-05-2024

• Registration:

• • Early registration deadline: 20-05-2024

  • Late registration deadline: 21-05-2024

• CBMS 2024: 26-28 June 2024

Sponsorship