IDEAL: Improved DEnse LocAL Contrastive Learning for Semi-Supervised Medical Image Segmentation, ICASSP 2023

Note: Polished code coming soon!

Hritam Basak, Soumitri Chattopadhyay*, Rohit Kundu*, Sayan Nag*, Rammohan Mallipeddi, "IDEAL: Improved DEnse LocAL Contrastive Learning for Semi-Supervised Medical Image Segmentation", IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2023.
*=equal contribution

Project Page | arXiv

Abstract

Due to the scarcity of labeled data, Contrastive Self-Supervised Learning (SSL) frameworks have lately shown great potential in several medical image analysis tasks. However, the existing contrastive mechanisms are sub-optimal for dense pixel-level segmentation tasks due to their inability to mine local features. To this end, we extend the concept of metric learning to the segmentation task, using a dense (dis)similarity learning for pre-training a deep encoder network, and employing a semi-supervised paradigm to fine-tune for the downstream task. Specifically, we propose a simple convolutional projection head for obtaining dense pixel-level features, and a new contrastive loss to utilize these dense projections thereby improving the local representations. A bidirectional consistency regularization mechanism involving two-stream model training is devised for the downstream task. Upon comparison, our IDEAL method outperforms the SoTA methods by fair margins on cardiac MRI segmentation.

Acknowledgement: This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1I1A3049810).

Citation

If you find this article useful in your research, consider citing us:

@inproceedings{basak2023ideal,
    author = {Hritam Basak and Soumitri Chattopadhyay and Rohit Kundu and Sayan Nag and Rammohan Mallipeddi},
    title = {IDEAL: Improved DEnse LocAL Contrastive Learning for Semi-Supervised Medical Image Segmentation},
    booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
    year = {2023}
}