## 4 Methodologies and Implementation
### Research on the Application of DDPM in Biomedical Image Segmentation
#### 4.1.2 Segmentation Algorithms Incorporating DDPM  
The segmentation algorithm combined with DDPM presents a cutting-edge approach to medical image segmentation, aiming to address challenges associated with noise and variability in imaging data. The masked-DDPM (mDDPM) method (see Figure 3) presents a significant advancement in the realm of unsupervised anomaly detection, providing a pathway for generating accurate anomaly maps and segmenting anomalies in medical images without the requirement for labeled data. The incorporation of masking mechanisms within the diffusion models not only enhances the precision of anomaly detection but also contributes to the acquisition of a comprehensive understanding of the structural characteristics of images. This study lays the foundation for further exploration of mDDPM in various medical imaging applications, potentially leading to improved diagnostic capabilities and clinical decision support systems in the future [58]. Ref. [59] introduces a novel Conditional Bernoulli Diffusion model for medical image segmentation. Unlike existing diffusion models that use Gaussian noise, BerDiff utilizes Bernoulli noise as the diffusion kernel, aiming to enhance the capacity of the diffusion  
Figure 3: mDDPM algorithm framework diagram. model for segmentation and produce more accurate and diverse segmentation masks. The authors emphasize the importance of providing accurate and diverse segmentation masks as valuable references for radiologists in clinical practice. The paper also references previous works that have combined diffusion models with segmentation tasks, but notes that these methods do not fully account for the discrete characteristic of segmentation tasks and still use Gaussian noise as their diffusion kernel. Ref. [60] introduces a novel approach, the Collectively Intelligent Medical Diffusion (CIMD) framework, which realistically models heterogeneity of segmentation masks without requiring additional network input during inference. The authors also propose a new evaluation metric, the Collective Insight (CI) Score, inspired by collective intelligence medicine. The study demonstrates the effectiveness of CIMD across three medical imaging modalities, outperforming existing ambiguous image segmentation networks in terms of both quantitative standards and qualitative results. The proposed approach not only improves accuracy but also preserves naturally occurring variation in segmentation. Additionally, the paper aligns with the interest of clinical practice by introducing a new metric to evaluate the diversity and accuracy of segmentation predictions. Overall, the CIMD framework presents a promising advancement in ambiguous medical image segmentation, offering both quantitative and qualitative improvements over existing methods. Ref. [61] addresses the vulnerability of deep learning models in biomedical image segmentation to adversarial attacks. The authors introduce the Adaptive Mask Segmentation Attack (ASMA), a novel algorithm capable of crafting targeted adversarial examples with high intersection-over-union rates and imperceptible perturbations. The experimental results demonstrate the effectiveness of ASMA in altering prediction masks to achieve misclassification, particularly in skin lesion and glaucoma optic disc segmentation tasks. The study highlights the potential security risks posed by adversarial examples in medical image analysis and emphasizes the need for robust defenses against such attacks. Furthermore, the paper provides valuable insights into the growing intersection of deep learning and medical imaging, shedding light on the implications of adversarial attacks for the reliability of automated diagnostic systems. The research opens avenues for future work in developing resilient deep learning models for biomedical image analysis and underscores the significance of addressing adversarial vulnerabilities in this critical domain. Ref.