Google Summer of Code 2023 Weekly Status Reports

Community Google Summer of Code
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We are going to try something new that we introduced in 2020. This is in addition to your blog post. The goal is to catch things early if you are behind in your timeline early.

@gsoc2023contributors,

Answer the following questions in a once-weekly post on Fridays:

  • What progress have you made this week?
  • What do I plan to do next week?
  • Have you had any blockers or issues that are impeding your project?

The point of these is to catch any issues and handle them early.

Any questions can be directed toward @judywawira, @sunbiz, @downey, or me.

Note: You aren’t required to do this until 2023-06-02T23:59:00Z and every Friday before 23:59 UTC. There is a short 24hr grace period.

Please note: The only posts on this topic should be the progress reports. No need to respond to posts, if there is an issue, use the project topics.

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Beginning 2023-06-02T23:59:00Z – you are all required to submit weekly reports.

Just answer ONLY these questions in an organized way once weekly on Friday.

  • What progress have you made this week?
  • What do I plan to do next week?
  • Have you had any blockers or issues that are impeding your project?

DO NOT miss any. You have a grace period of 24hrs but don’t be habitual about it.

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@r0bby We have to submit the report before next friday , Cause I still need to have the first meeting with mentors which was re-scheduled by @shbucher @sunbiz next week to discuss the project details and prepare the plan with them in accordance . Or something else ?

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Here is my project report for the Porject: Migrate Ehr to Laravel: User and Roles Module

Please verify whether it is correctly formatted or if I should write an extended form.

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Using the report format from last year…

What progress have you made this week?

  • Met with Mentor and discussed plan for the Project
  • Read papers and blogs on AI in medical imaging. Identified papers that discussed model architectures that produced good accuracies on medical imaging modalities
  • Identified data and model architecture for Mammography Tumor classification
  • Built a VGG16 Mammography Tumor Classification algorithm and updated it to my Github. Changes to model architecture and implementation to LH Radiology AI model service to happen later post OHIF webhook implementation.

What I plan to do next week?

  • Create a task/issue list concerning the development of the hook function in the OHIF extension. Based on the image modality and type of task (Segmentation, Classification), this function will call the appropriate model from the AI model service
  • Code deep dive and understanding for hook implementation
  • Start coding for the hook implementation tasks

Have you had any blockers or issues that are impeding your project?

  • None