Journal

Coach Meeting – 12/4/23

Key Takeaways

• Model has finished training and image visualization samples have been shared with client
• Something to think about is implementing allowing a direct download of source code rather than requiring cloning the repository

Next Steps

• Integrate a simple command line interface for client to use model
• Pull out handoff plan into its own section/page and expand on what we currently have
• Give specifics in handoff plan about next steps for model and how to continue iteration

Team Meeting – 11/26/23

Key Takeaways

• Model had training time error (too many epochs, so have to decrease the number)
• Training model again with less epochs, should finish training on 11/29/23

Next Steps

• Wait for model to finish training, create visualization code for the output images
• Finish the handoff plan

Coach Meeting – 11/17/23

Key Takeaways

• Could pass some files into the command line – maybe contained within a README
• Two different models are currently being trained, will take 3-6 days for these models to be completely trained

Next Steps

• Find time with client to meet and demo what we have
• Finish the documentation plan

Coach Meeting – 11/10/23

Key Takeaways

• APPLES reflection has been completed
• Demo shown – model outputs with specific labels for model with 30 training epochs

Next Steps

• Continue training model and creating other models
• Show client the demo we have prepared

Team Meeting – 11/06/23

Key Takeaways

• Team set-up LongLeaf for running the model
• Team uploaded the data into LongLeaf to be used for model training

Next Steps

• Run the model in LongLeaf for training and testing

Team Meeting – 11/04/23

Key Takeaways

• Team set-up Colab notebook and began coding model
  • Data loading and pre-processing
• Downloaded necessary datasets (BraTS 2020) – Brain Tumor MRIs
• Scheduled meeting with client for later this week (Thursday most likely)

Next Steps

• Meet tomorrow to continue model development
• Finalize meeting time with client this week

Coach Meeting – 11/03/23

Key Takeaways

• Team updated Ethics assignment with specific examples for each section
• Team is currently working on model development with hopes of demo in the coming 2 weeks
• Preparation for Tech Talk on Tuesday, slides are completed and demo is prepared

Next Steps

• Meet with client after initial model development for touchpoint and adjustments
• Continue working on model development

Coach Meeting – 10/27/23

Key Takeaways

• Ethics assignment should include specific examples (i.e. companies/softwares)
• Update the architecture diagram as we learn more about specifications
• Earlier we can demo, the easier it will be for us

Next Steps

• Within 2 weeks from today, having a fully functioning model that can segment ready for demo
• Meet with client to discover more about CLI vs UI requirements

Team Meeting – 10/17/23

Key Takeaways

• Met to set up Docker images to transfer into the Longleaf cluster
• Converted Docker image into Singularity image – to be used by Longleaf
• Worked on backend code development in Collab to be trained in Longleaf

Next Steps

• Continue working on Longleaf cluster to run previous code
• Continue working on backend code development

Client Meeting 3 – 10/11/23

Key Takeaways

• Team is caught up with GAN and model content
• Koushik has LongLeaf access now
• The pip file that Andrew shared with us should contain all of the dependencies
• Front-end should
  • Allow upload of folder, zip, or images
  • Will process through the GAN model
  • Return the segmented images

Next Steps

• Have the Docker instance set-up and run the initial code Andrew sent to better understand
  • Install Python in Docker
  • Pip Install
• Switch current code to function with CUDA
• Understand current code and functions

Client Meeting 2 – 09/25/23

Key Takeaways

• Andrew uploaded initial code for piping in CT scans and semantic segmentation
  • Will need to set up a python virtual environment
• Sent an email to our professor about access to LongLeaf and other computing resources from the University.
  • Could also concurrently setup LongLeaf since it is simple and easy.
• Reasoning for not using pre-trained GAN models?
  • There exists pre-trained segmentation model, mainly for the abdomen, but when Andrew tried to train these models on the brain, but there’s no control over the number of layers, parameters, etc…
  • Chose to set-up a tensorflow because they were slow and did not support multi-GPU processing.

Next Steps

• Send Andrew a single email with everyone’s dropbox email for the CT scan data
  • BRATS data for brain MRIs
• Will look at the code that Andrew has sent over so that we can understand implementation for CT scans.
• Setup the anaconda virtual environment to run the code and have the dependencies

Coach Meeting 2 – 09/22/23

Key Takeaways

• User Stories should be more broad and be less revealing of the implementation details
  • More user interactions with less focus on technical aspects
• Discussed getting access to LongLeaf, should follow up with Stotts for more information

Next Steps

• Remove technical details from User Stories
• Meet with client to discuss action items and deliverables to complete

Team Meeting 2 – 09/20/23

Key Takeaways

• Team chose to proceed with Brain MRI for our model
• Team Roles
  • Luke will handle front-end and help out with back-end if needed
  • Koushik and Daniel will handle main model development
  • Krishna to assist with both front-end and back-end development
• Team completed D1 Specifications and began to look at Ethics assignment

Next Steps

• Questions for Andrew:
  • Since there are already medical GAN models that segment images, what is stopping us from using these models?
  • Where do we want to host the web-app?
    • Tarheel
    • Firebase
    • GitHub pages

Coach Meeting 1 – 09/15/23

Key Takeaways

• Deliverables to be completed the Sunday following the classes/week written down for
• First client meeting set for today at 4:30PM
  • Should relay team understanding of project/scope to ensure alignment with client requirements
• Can detail implementation point priorities in user stories

Next Steps

• After client meeting, decide deliverables and roles going forth in the project
• Finish user stories and project concept to be completed and posted on website

Client Meeting 1 – 09/15/23

Key Takeaways

• All current code is written in Python with API calls
• Setting up an anaconda environment on Macs (or windows) should be good
• Focused on semantic segmentation for CT scans – skin, bone, tissue, etc…
• Previously had to manually segment CT scans by hand (took too long and tedious task)
  • Not most efficient training, low sample size
• Could train GAN for very specific objects in a CT scan (intracranial hemorrhages, tumors, etc…)
• Essentially our task is to create a “neurosurgeon” to label the CT scans
  • Initially the model will take a CT scan and be able to label the sections (blood, skin, brain, bone, etc…)
• Head CT is 512 images each 1024 x 1024
• Main project objectives:
  • Set up the GAN
  • Input Brain CT to the model for segmentation
• For running/training the model, can either run on:
  • University supercomputer
  • Andrew has an extra 3090
  • Could buy a shared network
  • Google Collab

Next Steps

• Send email to professor to inquire about possible usage of LongLeaf or Collab
• Decide if we want to do Brain (CT or MRI) or Spine
• Do more readings/research on GANs and usage (TensorFlow Documentation)
• Moving forward, weekly meetings and more/less as necessary
  • Primary mode of conversation to be text

09/08/2023 – Team Meeting 1

Key Takeaways

• Will make specific technical (tools, packages, etc…) and structural (project roles) decisions after initial client meeting
• Daniel Fang will take lead on Journal entries for internal and client-side meetings

Action Items

• Team to read up on GAN in medical fields
• Team to fully review previous COMP523 work – documentation, code, presentations, etc…