DINOv2 for cell classification

This project is a fork of the DINOv2 repo published by meta, aiming to use the methodology presented in their papers to train a series of model on blood white cells images.
Developping a foundation model for blood white cells is interesting for several reasons:

• The categories of blood white cells are not unanimous, and hematologists / datasets make different classes.
• Some blood white cells present mutations that are visible on images, and those distinguishible features could be embedded by the model

Installing

To install the project and the required packages that are necessary for this project use conda env install -f conda.yaml, then conda activate cell_sim and pip install -e ..

The dinov2 packages is then available from the conda env cell_sim for execution.

Data

The dataset implementation is in the file dinov2/data/datasets/custom_image_datasets.py, and retrieves all the images in all the folders indicated in the dataset paths. There are options to ignore a fraction of the images in each dataset given, and to check if the images are not corrupted before using them.

Training

Disclaimer:

Most of the code used for the training was used directly from the dinov2 repo, and not every function has been checked. Though it should be functional and do as intended, some aspects might not work as expected.

Config file

config files control every elements of the model, and of its training. The implementation made on your config files will merge with the one by default located at dinov2/configs/ssl_default_config.yaml.
In our case the minimal requirements for the train config files should be:

• dataset_path (List[str] or str) that indicates the path where the training data is located
• output_dir (str) that indicates the path where the logs, ckpts and models will be saved

Submit training

The script used to submit the training process is located at dinov2/run/train/train.py.
An example of command would be python dinov2/run/train/train.py --config-file dinov2/configs/train/vitl_cellsim_register.yaml --output-dir /home/guevel/OT4D/cell_similarity/vitl_register/ --partition hard --ngpus 2

that would launch the training on 1 node with 2 GPUs on the partition named hard.

This command makes a sh script with the required slurm constraints that is then executed.

The conda env previously created should be activated before launching this command.

Results