Organoid Segmentation Using Self-Supervised Learning: How Complex Should the Pretext Task Be?

Code repository for the conference paper "Organoid Segmentation Using Self-Supervised Learning: How Complex Should the Pretext Task Be?" published and presented at the International Conference on Biomedical and Bioinformatics Engineering (ICBBE) 2023.

Setup

The usage of a virtual environment is recommended. Dependencies tailored for Python 3.10.12. To install all dependencies, run:

pip install --upgrade pip setuptools wheel
pip install -r requirements.txt

Dataset can be downloaded from OrganelX. Folder structure should be:

organoid_data/
organoid_data/1CA5KMCR7K7MT53OS7FG/
etc.

Repository contains dummy data set to test functionality.

Usage

As described in the paper, training is divided into pretext training and downstream training.

Pretext

Pretext training can be done using:

python train_pretext.py --task1=[task option] [--task2=[task option]] [--dummy True]

Testing of pretext trained models can be done using:

python test_pretext.py --task1=[task option] [--task2=[task option]] [--dummy True]

[task option] consists of {b,d,s,r,B,D,S,R,j,p} and represents the different self-prediction/ innate relationship pretext tasks. Self-prediction pretext tasks can be combined by specifying the second task --task2 as well.

--dummy True specifies the use of the dummy data set rather than the actual data set. Default is set to False.

Downstream

Note: testing of the pretext trained models should be done before downstream training as the best pretext model is used for downstream training.

Downstream training can be done using:

python train_downstream.py --task1=[task option] [--task2=[task option]] [--dummy True]

Testing of downstream trained models can be done using:

python test_downstream.py --task1=[task option] [--task2=[task option]] [--dummy True]

[task option] consists of {b,d,s,r,B,D,S,R,j,p} and represents the different self-prediction/ innate relationship pretext tasks. Self-prediction pretext tasks can be combined by specifying the second task --task2 as well.

--dummy True specifies the use of the dummy data set rather than the actual data set. Default is set to False.