## 4 Methodologies and Implementation
### Research on the Application of DDPM in Biomedical Image Segmentation
#### 4.1.2 Segmentation Algorithms Incorporating DDPM
Overall, ESDiff is a promising approach for enhancing the quality of low-quality retinal images and accurately diagnosing various diseases. Ref. [67] presents a novel approach to unsupervised brain anomaly detection and segmentation using deep generative models. The authors demonstrate the effectiveness of their method on a dataset of brain MRI scans, achieving high accuracy and efficiency in detecting anomalies without the need for manual labeling. They compare their approach to other state-of-the-art models and show that their method outperforms them in terms of both accuracy and speed. The potential implications of this research for the field of neurology and medical imaging are significant, as it could lead to improved patient care and treatment outcomes. Overall, this paper provides a valuable contribution to the field of medical imaging and highlights the potential of deep generative models for unsupervised anomaly detection. Ref. [68] presents a novel approach that integrates text-based attention mechanisms with diffusion models to advance medical image segmentation. The DTAN framework effectively directs the network's focus towards crucial regions, leveraging the diffusion model's inherent information propagation capabilities to achieve precise segmentation outcomes. Key contributions include the Feature Enhancement Module (FEM) for capitalizing on multi-scale information and the incorporation of an auxiliary classification task to refine segmentation accuracy. The authors conducted comprehensive model comparisons and ablation experiments, demonstrating the superior performance of DTAN on datasets such as Kvasir-Sessile and GlaS. The integration of text attention and diffusion models has yielded a robust enhancement in segmentation performance, setting a precedent for future research trajectories aimed at advancing medical image analysis. Ref. [69] proposes a novel approach called DiffuseExpand for expanding datasets for 2D medical image segmentation using Diffusion Probabilistic Models (DPMs). The approach addresses the challenges of data scarcity and diversity in synthesized images with paired segmentation masks. Through comparison and ablation experiments on COVID-19 and CGMH Pelvis datasets, the authors demonstrate the effectiveness of DiffuseExpand in expanding medical image segmentation datasets. The results show that DiffuseExpand can synthesize high-quality and diverse Image-Mask pairs, which can enhance the performance of segmentation models. The approach can be applied to other types of medical image datasets beyond COVID-19 and CGMH Pelvis datasets. Overall, DiffuseExpand offers a promising solution for expanding medical image segmentation datasets and improving the performance of segmentation models. Ref. [70] introduces a novel framework for skin lesion segmentation called DermoSegDiff. This framework addresses the challenges of accurately delineating skin lesions by prioritizing boundary information during training and incorporating a denoising network to enhance the understanding of noise-semantic relationships. The proposed approach introduces a novel loss function that emphasizes the importance of segmentation's boundary region and assigns it higher weight during training. Additionally, a U-Net-based denoising network is presented, which effectively models noise-semantic information and leads to performance improvement. Ref. [71] introduces a novel approach, PatchDDM, to address the challenge of processing large three-dimensional (3D) volumes in medical image analysis. The authors propose architectural changes to the state-of-the-art diffusion model implementation, enabling training on large 3D volumes with commonly available GPUs. This approach improves speed and memory efficiency by training the diffusion model only on coordinate-encoded patches of the input volume, reducing memory consumption and speeding up the training process. Additionally, the proposed method allows processing large volumes in their full resolution without needing to split them into patches during sampling. Ref.