Stroke lesion volume is a key radiologic measurement for assessing the
prognosis of Acute Ischemic Stroke (AIS) patients, which is challenging to be
automatically measured on Non-Contrast CT (NCCT) scans. Recent diffusion
probabilistic models have shown potentials of being used for image
segmentation. In this paper, a novel Synchronous image-label Diffusion
Probability Model (SDPM) is proposed for stroke lesion segmentation on NCCT
using Markov diffusion process. The proposed SDPM is fully based on a Latent
Variable Model (LVM), offering a complete probabilistic elaboration. An
additional net-stream, parallel with a noise prediction stream, is introduced
to obtain initial noisy label estimates for efficiently inferring the final
labels. By optimizing the specified variational boundaries, the trained model
can infer multiple label estimates for reference given the input images with
noises. The proposed model was assessed on three stroke lesion datasets
including one public and two private datasets. Compared to several U-net and
transformer-based segmentation methods, our proposed SDPM model is able to
achieve state-of-the-art performance. The code is publicly available.

本文提出了一种基于扩散概率模型的非对比 CT 扫描中卒中病灶分割方法，该方法完全基于隐变量模型，在噪声预测流的帮助下能够高效地推断出最终标签，经多组数据集验证，证实其达到了最先进的性能。

应用于非对比 CT 中卒中灶分割的同步图像 - 标签扩散概率模型

Synchronous Image-Label Diffusion Probability Model with Application to  Stroke Lesion Segmentation on Non-contrast CT

Popular reinforcement learning (RL) algorithms tend to produce a unimodal
policy distribution, which weakens the expressiveness of complicated policy and
decays the ability of exploration. The diffusion probability model is powerful
to learn complicated multimodal distributions, which has shown promising and
potential applications to RL. In this paper, we formally build a theoretical
foundation of policy representation via the diffusion probability model and
provide practical implementations of diffusion policy for online model-free RL.
Concretely, we character diffusion policy as a stochastic process, which is a
new approach to representing a policy. Then we present a convergence guarantee
for diffusion policy, which provides a theory to understand the multimodality
of diffusion policy. Furthermore, we propose the DIPO which is an
implementation for model-free online RL with DIffusion POlicy. To the best of
our knowledge, DIPO is the first algorithm to solve model-free online RL
problems with the diffusion model. Finally, extensive empirical results show
the effectiveness and superiority of DIPO on the standard continuous control
Mujoco benchmark.

本文利用扩散概率模型提出了一种新的随机策略表示方法，并证明了它对于多模态分布的优越性，进而应用到无模型在线强化学习中，提出 DIPO 算法，在标准连续控制 Mujoco 基准中取得了显著优势。