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Unsupervised Visualisation of Medical Image Datasets

This repository contains the codes to train a $t$-SimCNE model for medical images. You can find our paper here: Unsupervised Visualisation of Medical Image Datasets

Citation

If you use this code, kindly cite our paper:

@misc{nwabufo2024selfsupervised,
      title={Self-supervised Visualisation of Medical Image Datasets}, 
      author={Ifeoma Veronica Nwabufo and Jan Niklas Böhm and Philipp Berens and Dmitry Kobak},
      year={2024},
      eprint={2402.14566},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Installation

$t$-SimCNE is available as a package. You could install it by running

pip install tsimcne

or you can clone this repository.

git clone https://github.com/berenslab/medical-t-simcne
cd medical-t-simcne
pip install .

Training a $t$-SimCNE model on MedMNIST dataset

#import libraries
import numpy as np
import medmnist.dataset
from tsimcne.imagedistortions import *
from tsimcne.tsimcne import TSimCNE
from evaluation.eval import knn_acc,silhouette_score_
from torch.utils.data import ConcatDataset

#load the data
root='datasets'
dataset_train = medmnist.dataset.BloodMNIST(root=root, split='train', transform=None,target_transform=None, download=True)
dataset_test = medmnist.dataset.BloodMNIST(root=root, split='test', transform=None, target_transform=None, download=True)
dataset_val = medmnist.dataset.BloodMNIST(root=root, split='val', transform=None, target_transform=None, download=True)
dataset_full = [dataset_train, dataset_test,dataset_val]

for dataset in dataset_full:
        dataset.labels = dataset.labels.squeeze()
dataset_full_ = ConcatDataset(dataset_full)

labels = np.array([lbl for img, lbl in dataset_full_])


batch_size=1024
total_epochs=[1000,50,450]

# You can also define your custom augmentations by passing a 'data_transform' parameter.
# For more details check scripts/mnist.py or 
# read the documentation here [https://t-simcne.readthedocs.io/]  
tsimcne = TSimCNE(batch_size=batch_size, total_epochs=total_epochs) 
Y = tsimcne.fit_transform(dataset_full_)

#get the metrics
kNN_score=knn_acc(Y,labels)
sil_score=silhouette_score_(Y,labels)

#visualise the results
fig, ax = plt.subplots()
ax.scatter(*Y.T, c=labels)
ax.set_title(f"$k$NN acc. = {kNN_score}% sil score = {sil_score}")
fig.savefig("tsimcne.png")

Figures

To reproduce the figures, you can run the respective python files in the plot folder at the root of this directory.

Embeddings

To get the embeddings run the respective python files in the scripts folder at the root of the directory.