A Comparative Study on Diffusion Sampling Methods Across Diverse Medical Imaging Modalities

Muhammad Ali Farooq, Ayman Abaid, Ihsan Ullah, Peter Corcoran; Proceedings of the Asian Conference on Computer Vision (ACCV) Workshops, 2024, pp. 193-206

The evaluation of diffusion-based image sampling methods is pivotal in improving the quality and reliability of synthetic data generation, particularly in medical imaging applications. Medical imaging requires high precision and fidelity, as even subtle artifacts or inconsistencies can significantly impact clinical decision-making. This study examines the effectiveness of four different image sampling techniques across various medical imaging modalities, focusing on dermoscopic skin lesion data, computed tomography angiography for Type B Aortic Dissection, and chest X-ray imaging. By systematically assessing these methods, we aim to enhance the fidelity of synthetic datasets, ensuring they more closely resemble real-world clinical data thereby supporting more accurate diagnostics, treatment planning, and prognostic predictions. In this work, we evaluate the performance of four different sampling techniques by incorporating Euler, Euler A, Denoising Diffusion Implicit Mode (DDIM), and Pseudolinear Multistep (PLMS) approaches for medical image synthesis. The study utilizes quantitative metrics including Structural Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) to assess the realism and structural integrity of the generated images. Additionally, we employed t-SNE visualization to illustrate the latent feature representations of rendered synthetic medical images, providing an intuitive understanding of the underlying structure. We also analyzed and compared the computational complexity associated with each image sampling technique, offering insights into the efficiency of different approaches. The generated medical images are available at Diffusion-Sampling-for-Medical-Image-Synthesis.