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CervicAI

*Video Demo on Youtube

Who do we address?

  • Patitents: uploading your cervical images and know the prodiction in few minutes: image

  • Doctors: screening cervical images to save you a lot of time: image

  • Labeling expert: Automatically label unlabeled data for you: image

  • Machine learning enginers / Data scientists:
    image We do

    • How to find the dupicated images
    • How to find the similar images
    • How to find the bias images
    • How to pick the important images
    • How to train the imbalanced images

    We don't

    • address bias by adjusting layers, hyper-parameters
    • get high score by fine turn anyting
    • have any INNOVATION but leveraging existed/proved technologies such as PCA, SimCLR, etc.

What technologies we integrated?

  • Semi-Self supervised learning: SimCLR(Google), SwAV(Facebook), Dino(Facebook)
    • Why Semi-Self supervised learning: domain expert (doctor) is expensive, labling time is very long.
  • Active learning: Detectron2(Facebook)
    • Why active learning: Pixies to machine is diffrent to human beings, machine can do better to choose what they need.

Usage

1) Patients:

2) Doctors:

The default path should be ./experiemnt/data. You can make new directory /experiment under the root, extract the data, then rename the directory name to data. You can also open nu_gan.py to change the default path.

3) Labeling expert:

Three tasks can be chosen using flags as follows.

  • Unsupervised Cell-level Classification:
python nu_gan.py --task 'cell_representation'
  • Unsupervised Image-level Classification:
python nu_gan.py --task 'image_classification'
  • Neuclei Segmentation:
python nu_gan.py --task 'cell_segmentation'

For convenience, the parameters for training is stored in nu_gan.py, which can be changed easily.