BTCV dataset
收藏Figshare2025-05-16 更新2026-04-08 收录
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https://figshare.com/articles/dataset/BTCV_dataset/29077214/1
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资源简介:
LKDA-Net: 基于大核深度可分离卷积注意力的高效3D医学图像分割模型<br> <br><br>LKDA-Net 是一种轻量化的3D医学图像分割网络,通过 大核深度可分离卷积注意力(LKD Attention) 和 跳跃连接融合模块,在降低计算复杂度的同时实现高精度分割。本仓库提供完整的训练、推理及可视化代码。<br><br>We propose <b>a</b> lightweight three-dimensional convolutional network, LKDA-Net, <b>for</b> efficient <b>and</b> accurate three-dimensional volumetric segmentation. This network adopts <b>a</b> large-kernel depthwise convolution attention mechanism to simulate <b>the</b> self-attention mechanism <b>of</b> Transformers. Firstly, inspired <b>by</b> <b>the</b> Swin Transformer module, we investigate different-sized large-kernel convolution attention mechanisms to obtain larger global receptive fields, <b>and</b> replace <b>the</b> MLP <b>in</b> <b>the</b> Swin Transformer <b>with</b> <b>the</b> Inverted Bottleneck <b>with</b> Depthwise Convolutional Augmentation to reduce channel redundancy <b>and</b> enhance feature expression <b>and</b> segmentation performance. Secondly, we propose <b>a</b> skip connection fusion module to achieve smooth feature fusion, enabling <b>the</b> decoder to effectively utilize <b>the</b> features <b>of</b> <b>the</b> encoder. Finally, through experimental evaluations <b>on</b> <b>three</b> <b>public</b> <b>datasets</b>, <b>namely</b> <b>Synapse</b>, <b>BTCV</b> <b>and</b> <b>ACDC</b>, <b>LKDA-Net</b> <b>outperforms</b> <b>existing</b> <b>models</b> <b>of</b> <b>various</b> <b>architectures</b> <b>in</b> <b>segmentation</b> <b>performance</b> <b>and</b> <b>has</b> <b>fewer</b> <b>parameters</b>.
提供机构:
Li, Ming
创建时间:
2025-05-16



