Iteration 3 LCP

Iteration goals:

In this iteration we will make a progress in the project. After making DeepMedic work, and only reading about Cascaded-FCN for RCF and Faster-RNNLM for classifier, we will dive into those 2 projects, understanding what's going on there. Plus, taking what we need - RCF and classifier. Please review the important links found downstairs and see all the work we've done.

Lists of goals:

• Improve DeepMedic work
• Improve Yolo work
• Finish project
• Project book
• Getting ready for final exam.

use cases:

• Learning.
• Classifying.

Retro prospective - goals and results:

• To be written in the end of iteration

Roles:

Team: Alon and stav
Generally:

• Alon - Classifier
• Stav - DeepMedic

Issue Management:

Issue Board

Iteration 3 Issues

Tests:

• According to tests plan as shown on report - functional and non-functional tests, being documented.
• Checks for synthetic data if needed.
• Evaluate Segmentation

Points Summary:

• Yolo - data modification - 13 points
• DeepMedic - accuracy - 13 points
  
• Final video - 5 points
  
• Project book - 8 points
  
• Expanding examinations - 5 points
  
• Code tests - 5 points
• Exam preparations - 8 points
  
• Code factoring - 5 points
  

Total: 62 points

LCF - Learning, Classifying and Presentation

This stage actually finishes the learning and classifying stage. Plus, it deals with the presentation on June 19 2017.

Important Links

• Working Document - All information gathered through this iteration.
• Research Blog

Iteration Summary:

• As a summary,

To improve:

• We

To sum it up:

• Now entering

Next Iteration:

Iteration 4