用空间注意力和潜在嵌入加速医学图像分割的扩散模型
Conditional diffusion model with spatial attention and latent embedding for medical image segmentation
- 引入时空注意力机制，提升关键区域分割精度
- 在三个数据集上实现更高Dice与mIoU得分
- 通过随机潜变量减少训练和采样步数，提速明显
扩散模型广泛应用于高质量图像与视频生成。本文提出一种新型条件扩散模型cDAL（带空间注意力和潜在嵌入的条件扩散模型），用于医学图像分割。cDAL在扩散过程每一步使用基于卷积神经网络的判别器，区分生成标签与真实标签；基于判别器学习的特征计算空间注意力图，引导模型更准确地生成输入图像中具有区分性的区域。此外，在每一层引入随机潜在嵌入，显著减少训练与采样所需时间步数，使模型速度远超其他扩散分割方法。我们在三个公开医学图像分割数据集（MoNuSeg、Chest X-ray、Hippocampus）上测试，结果在定性和定量上均优于当前最优算法，获得更高的Dice分数与平均交并比（mIoU）。代码已开源：https://github.com/Hejrati/cDAL/
原文摘要 · Abstract (English)
Diffusion models have been used extensively for high quality image and video generation tasks. In this paper, we propose a novel conditional diffusion model with spatial attention and latent embedding (cDAL) for medical image segmentation. In cDAL, a convolutional neural network (CNN) based discriminator is used at every time-step of the diffusion process to distinguish between the generated labels and the real ones. A spatial attention map is computed based on the features learned by the discriminator to help cDAL generate more accurate segmentation of discriminative regions in an input image. Additionally, we incorporated a random latent embedding into each layer of our model to significantly reduce the number of training and sampling time-steps, thereby making it much faster than other diffusion models for image segmentation. We applied cDAL on 3 publicly available medical image segmentation datasets (MoNuSeg, Chest X-ray and Hippocampus) and observed significant qualitative and quantitative improvements with higher Dice scores and mIoU over the state-of-the-art algorithms. The source code is publicly available at https://github.com/Hejrati/cDAL/.
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