Liver segmentation and disease detection machine learning project using pytorch and Monai

I'm embarking on an ambitious project aiming to leverage the power of machine learning for outcome prediction, specifically utilizing image data. My goal is to create a system capable of analyzing visual information and predicting future outcomes based on this data. The project is at a planning stage, and I'm exploring potential processes - whether real-time processing is a necessity or if batch processing can achieve the desired results.

Project- Liver Segmentation and disease detection in liver using pytorch and Monai

I want all the files including datasets and code files.
And a video about how to download source code and run the project on my laptop.



**Key Requirements:**
- **Expertise in Machine Learning:** You must have a solid understanding of machine learning algorithms suitable for prediction tasks.
- **Experience with Image Data:** Proficiency in handling and processing image data for machine learning applications is essential.
- **Real-time vs Batch Processing:** While I'm open to suggestions, the ideal candidate will have experience with both real-time and batch processing methods and can recommend the best approach for this project.
- **Problem-Solving Skills:** Ability to identify the most efficient algorithms and techniques for predicting outcomes from image data.

**Nice to Have:**
- Prior projects involving outcome predictions based on visual inputs.
- Knowledge of Python, PyTorch and Monai

I'm looking for a freelancer who can guide me through the decision-making process on real-time versus batch processing and help develop a robust predictive model. Your input will be invaluable in shaping the direction of this project.