Biomedical Image Segmentation Using Denoising Diffusion Probabilistic Models: A Comprehensive Review and Analysis
Source abstract
Abstract
Biomedical image segmentation plays a pivotal role in medical imaging, facilitating precise identification and delineation of anatomical structures and abnormalities. This review explores the application of the Denoising Diffusion Probabilistic Model (DDPM) in the realm of biomedical image segmentation. DDPM, a probabilistic generative model, has demonstrated promise in capturing complex data distributions and reducing noise in various domains. In this context, the review provides an in-depth examination of the present status, obstacles, and future prospects in the application of biomedical image segmentation techniques. It addresses challenges associated with the uncertainty and variability in imaging data analyzing commonalities based on probabilistic methods. The paper concludes with insights into the potential impact of DDPM on advancing medical imaging techniques and fostering reliable segmentation results in clinical applications. This comprehensive review aims to provide researchers, practitioners, and healthcare professionals with a nuanced understanding of the current state, challenges, and future prospects of utilizing DDPM in the context of biomedical image segmentation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · Medical Image Segmentation Techniques · Radiomics and Machine Learning in Medical Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 101408, China
References (100+)
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