The document discusses various aspects of machine learning application in medical imaging, including model comparisons and text encoding. It emphasizes the use of convolutional neural networks and graph-based methods for tasks such as brain segmentation and surgical planning. The conclusion highlights the significance of these techniques in disease diagnosis and treatment monitoring.

3. machine learning

5. Model Comparison

6. Conclusion

9. LLMModel

10. Text Encoding

11. Conclusion

13. Unlock
Introduction

14. Data Analysis

15. Merchandising Strategies

16. Conclusion

17. 3DBrain Segmentation:An
in-depth analysis

18. medical imagi
ng

19. anatomical variations image artifacts

20. machine learning convolutional neural networks graph-based
methods

21. neurosurgi
cal pl
anningdisease
diagnosis treatment monitoring
Cl
i
ni
calApplicat
ions

22. medical imaging
Conclusion

23. Thank You