Semi-Supervised Volumetric Medical Image Segmentation via Class Prototype Guided Distribution-Aligned Representation Learning

We present SemiCRL, a novel framework for volumetric medical image segmentation that formulates an innovative contrastive learning methodology in a semi-supervised learning setting. We leverage the pseudo-labels generated in semi-supervised learning to guide the selection of negative samples for our...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1931 - 1935
Hlavní autoři: Kong, Xiangyu, Ren, Zeyu, Liu, Lu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 14.04.2024
Témata:
Benchmark testing
Contrastive Learning
Image segmentation
Medical Image Segmentation
Prototypes
Representation learning
Self-supervised learning
Semi-Supervised Learning
Semisupervised learning
Signal processing
ISSN:2379-190X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:We present SemiCRL, a novel framework for volumetric medical image segmentation that formulates an innovative contrastive learning methodology in a semi-supervised learning setting. We leverage the pseudo-labels generated in semi-supervised learning to guide the selection of negative samples for our contrastive learning, aiming to alleviate the class collision issue and learn enhanced class-discriminative latent representations. However, to address the inaccuracies in pseudo-labels, which stem from the empirical distribution misalignment between labeled and unlabeled data, we introduce a pseudo-label refinement strategy based on class prototypes computed from learned latent representations. Furthermore, our contrastive learning utilizes class prototypes as powerful reference points to enforce the alignment of latent-space distribution of labeled and unlabeled data, thus fostering knowledge transfer from labeled to unlabeled data, which in turn enhances the generation of accurate pseudo-labels in semi-supervised learning. Experiments on two public medical image datasets demonstrate our proposed method outperforms existing state-of-the-art semi-supervised approaches.
ISSN:2379-190X
DOI:10.1109/ICASSP48485.2024.10446473