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Two-Stage Self-Supervised Contrastive Learning Aided Transformer for Real-Time Medical Image Segmentation

journal contribution
posted on 2024-01-19, 04:28 authored by A Qayyum, Imran RazzakImran Razzak, M Mazher, T Khan, W Ding, S Niederer
The availability of large, high-quality annotated datasets in the medical domain poses a substantial challenge in segmentation tasks. To mitigate the reliance on annotated training data, self-supervised pre-training strategies have emerged, particularly employing contrastive learning methods on dense pixel-level representations. In this work, we proposed to capitalize on intrinsic anatomical similarities within medical image data and develop a semantic segmentation framework through a self-supervised fusion network, where the availability of annotated volumes is limited. In a unified training phase, we combine segmentation loss with contrastive loss, enhancing the distinction between significant anatomical regions that adhere to the available annotations. To further improve the segmentation performance, we introduce an efficient parallel transformer module that leverages Multiview multiscale feature fusion and depth-wise features. The proposed transformer architecture, based on multiple encoders, is trained in a self-supervised manner using contrastive loss. Initially, the transformer is trained using an unlabeled dataset. We then fine-tune one encoder using data from the first stage and another encoder using a small set of annotated segmentation masks. These encoder features are subsequently concatenated for the purpose of brain tumor segmentation. The multiencoder-based transformer model yields significantly better outcomes across three medical image segmentation tasks. We validated our proposed solution by fusing images across diverse medical image segmentation challenge datasets, demonstrating its efficacy by outperforming state-of-the-art methodologies.

History

Journal

IEEE Journal of Biomedical and Health Informatics

Volume

PP

Pagination

1-10

Location

United States

ISSN

2168-2194

eISSN

2168-2208

Language

eng

Issue

99

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

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