CSC2529 (Computational Imaging) 2022 project

Project title: Brain tumor MRI synthetic image generation using aggregation of GANs.

Important files

Project report

• https://github.com/edithal-14/csc2529-2022-project/blob/master/poster_and_report/imaging_project_report/report.pdf

Poster presentation

• https://github.com/edithal-14/csc2529-2022-project/blob/master/poster_and_report/imaging_project_2022_poster.pdf

Project proposal

• https://github.com/edithal-14/csc2529-2022-project/blob/master/proposal/proposal.pdf

Source code

• WGAN

  • https://github.com/edithal-14/csc2529-2022-project/blob/master/src/WGAN.ipynb

• Upscaling

  • https://github.com/edithal-14/csc2529-2022-project/blob/master/src/Upscaling.ipynb

• UNet GAN

  • https://github.com/edithal-14/csc2529-2022-project/blob/master/src/UNETGAN.ipynb

• Style Transfer

  • https://github.com/edithal-14/csc2529-2022-project/blob/master/src/StyleTransfer.ipynb

• DCGAN

  • https://github.com/edithal-14/csc2529-2022-project/blob/master/src/DCGAN.ipynb

• Inference Engine

  • https://github.com/edithal-14/csc2529-2022-project/blob/master/src/Inference_engine.ipynb

• Classification model

  • https://github.com/edithal-14/csc2529-2022-project/blob/master/src/Classification_.ipynb

• Aggregation

  • https://github.com/edithal-14/csc2529-2022-project/blob/master/src/Aggregation.ipynb

• Result generation

  • https://github.com/edithal-14/csc2529-2022-project/blob/master/src/results.ipynb

Directory structure

notes

Notes on literature survey and brainstorming ideas for deciding the project idea

papers

Research papers consulted for literature survey

poster_and_report

Contains project proposal, poster presentation and final report along with templates

proposal

Contains latex files for the project proposal

results

Results from ablation study of various GAN models

src

Source code and analysis for our experiments

tutorials

Tutorial code, notes and references to learn about various GAN models