用生成嵌入翻译实现高效精准的医学图像分割。
GET: Generative Embedding Translation for Medical Image Segmentation
- 在冻结的Stable Diffusion潜空间中逐级转换图像嵌入为掩码嵌入。
- 相比最强基线，Dice和IoU提升0.93%与1.26%，参数减少31.41%。
- 适合追求高精度与低计算开销的医学图像分割场景。
生成式分割通过在学习的潜在表示上操作，替代直接的像素级预测，但有效的图像到掩码转换需兼顾目标结构保持与计算效率。我们提出生成嵌入翻译（GET），一种结构化的嵌入-翻译框架，在冻结的Stable Diffusion VAE潜空间内逐步将图像嵌入转化为掩码嵌入。GET采用具有107万可训练参数的U-Net风格嵌入翻译网络，结合移动瓶颈卷积、下采样自注意力和多尺度特征增强，实现局部建模、全局上下文捕捉与多尺度优化。在五个医学分割数据集上，GET优于生成式、CNN与Transformer基线。相比最强生成基线GMS，GET平均Dice与IoU分别提升0.93%与1.26%，HD95降低0.81像素，且参数量减少31.41%。在双向BUS-BUSI域偏移下，其Dice与IoU进一步提升3.51%与3.39%，HD95降低27.37像素。代码已开源。
原文摘要 · Abstract (English)
Generative segmentation provides an alternative to direct pixel-wise prediction by operating on learned latent representations, but effective image-to-mask translation must preserve target structure while remaining computationally efficient. We propose Generative Embedding Translation (GET), a structured embedding-translation framework that progressively transforms image embeddings into mask embeddings within the frozen latent space of a Stable Diffusion VAE. GET uses a U-Net-style Embedding Translation Network with 1.07M trainable parameters, combining Mobile Bottleneck Convolutions, Subsampled Self-Attention, and Multi-scale Feature Enrichment for local modeling, global context, and multi-scale refinement. Across five medical segmentation datasets, GET outperforms generative, CNN, and Transformer baselines. Compared with the strongest generative baseline, GMS, GET improves average Dice and IoU by 0.93% and 1.26%, reduces HD95 by 0.81 pixels, and uses 31.41% fewer trainable parameters. Under bidirectional BUS-BUSI domain shift, GET further improves Dice and IoU by 3.51% and 3.39%, while reducing HD95 by 27.37 pixels. Our code is available at: https://github.com/maklachur/GET.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。