Medical Image Segmentation via Sparse Coding Decoder

Transformers have achieved significant success in medical image segmentation, owing to their capability to capture long-range dependencies. Previous studies have employed either pure Transformer or hybrid CNN-Transformer architectures in the encoder module to enhance their ability to extract more co...

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Veröffentlicht in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 1 - 5
Hauptverfasser: Zeng, Long, Zhu, Mingwei, Wu, Kaigui, Li, Zefang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 06.04.2025
Schlagworte:
Biomedical imaging
Convolution
Convolutional codes
Decoding
Feature extraction
Image coding
Image segmentation
Medical image segmentation
Noise
Sparse Coding
Transformer
Transformers
Vectors
ISSN:2379-190X
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Zusammenfassung:Transformers have achieved significant success in medical image segmentation, owing to their capability to capture long-range dependencies. Previous studies have employed either pure Transformer or hybrid CNN-Transformer architectures in the encoder module to enhance their ability to extract more complex features. However, these models still exhibit limitations in fine-grained local feature extraction and effectively suppressing irrelevant information. To address this issue, a convolution sparse vector coding-based decoder is proposed, namely the CAScaded multi-layer Convolutional Sparse vector Coding DEcoder (CASCSCDE), which suppresses noise by refining the feature representation to be more sparse and accurate through sparse coding and localized convolution, effectively minimizing less important, noisy components. To demonstrate the effectiveness and versatility of our CASCSCDE, we incorporate our decoder into both pure Transformer and hybrid CNN-Transformer models, such as SwinUNet and TransUNet. Our experiments demonstrate that integrating CASCSCDE into the models significantly enhances segmentation performance. The CASCSCDE opens new ways for constructing decoders based on convolutional sparse vector coding.
ISSN:2379-190X
DOI:10.1109/ICASSP49660.2025.10889260