.png){kind=icon}
返回
Explicit-implicit priori knowledge-based diffusion model for generative medical image segmentation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2024.112426.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The diffusion probabilistic model (DPM) has achieved unparalleled results in current image generation tasks, and some recent research works employed it in several computer vision tasks, such as image super-resolution, object detection, etc. Thanks to DPM's superior ability to generate fine-grained details, these research efforts have yielded significant successes. In this paper, we propose a new DPM-based generative medical image segmentation method, named EIDiffuSeg. Specifically, we first construct an explicit-implicit aggregation priori knowledge with directional supervision ability by mining the semantic distribution pattern in the frequency and spatial domains. Then, the explicit-implicit aggregation priori knowledge is integrated into the different encoding stages of the denoising backbone network using a novel unsupervised priori knowledge induction strategy, which can guide the model to generate a segmentation mask of the region of interest directionally from a random inference process. We evaluate our method on three medical image segmentation benchmark datasets with different modalities and achieve the best segmentation results compared to state-of-the-art methods. Especially, compared to several current diffusion-based image segmentation methods, we achieved a 9% Dice improvement in the polyp segmentation benchmark. Our code will be available at https://github.com/Notmezhan/EIDiffuSeg.
Keyword:
Diffusion probabilistic model
Medical image segmentation
Attention mechanism
Deep learning
Generative model
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
暂无机构信息
引用论文
SRDiff: Single image super-resolution with diffusion probabilistic modelsSRDiff: 具有扩散概率模型的单幅图像超分辨率.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians在结肠镜检查中准确突出显示息肉的wm-dova地图: 医生的验证与显著性地图
PREAI
分享收藏A New Short Track Test to Estimate the VO2max and Maximal Aerobic Speed in Well-Trained Runners
PREAI
分享收藏Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation基于多标记深度网络和极坐标变换的视盘和视杯联合分割OAAI
OAAI分享收藏分享收藏UNet plus plus : Redesigning Skip Connections to Exploit Multiscale Features in Image SegmentationUNet plus plus: 重新设计跳过连接以利用图像分割中的多尺度特征OAAI
OAAI分享收藏
没有更多内容
没有更多内容