Transformers have achieved significant success in medical image segmentation,
owing to its capability to capture long-range dependencies. Previous works
incorporate convolutional layers into the encoder module of transformers,
thereby enhancing their ability to learn local relationships among pixels.
However, transformers may suffer from limited generalization capabilities and
reduced robustness, attributed to the insufficient spatial recovery ability of
their decoders. To address this issue, A convolution sparse vector coding based
decoder is proposed , namely CAScaded multi-layer Convolutional Sparse vector
Coding DEcoder (CASCSCDE), which represents features extracted by the encoder
using sparse vectors. To prove the effectiveness of our CASCSCDE, The
widely-used TransUNet model is chosen for the demonstration purpose, and the
CASCSCDE is incorporated with TransUNet to establish the TransCASCSCDE
architecture. Our experiments demonstrate that TransUNet with CASCSCDE
significantly enhances performance on the Synapse benchmark, obtaining up to
3.15\% and 1.16\% improvements in DICE and mIoU scores, respectively. CASCSCDE
opens new ways for constructing decoders based on convolutional sparse vector
coding.

基于卷积稀疏向量编码的解码器，名为 CASCSCDE，在医学图像分割中有效地提升了 Transformers 模型的性能。