Review of diffusion models and its applications in biomedical informatics

محفوظ في:
التفاصيل البيبلوغرافية
الحاوية / القاعدة:BMC Medical Informatics and Decision Making vol. 25 (2025), p. 1-22
المؤلف الرئيسي: Luo, Jiawei
مؤلفون آخرون: Yang, Liren, Liu, Yan, Hu, Changbao, Wang, Grant, Yang, Yan, Tie-Lin, Yang, Zhou, Xiaobo
منشور في:
Springer Nature B.V.
الموضوعات:
الوصول للمادة أونلاين:Citation/Abstract
Full Text
Full Text - PDF
الوسوم: إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

MARC

LEADER 00000nab a2200000uu 4500
001 3268430055
003 UK-CbPIL
022 |a 1472-6947 
024 7 |a 10.1186/s12911-025-03210-5  |2 doi 
035 |a 3268430055 
045 2 |b d20250101  |b d20251231 
084 |a 58451  |2 nlm 
100 1 |a Luo, Jiawei 
245 1 |a Review of diffusion models and its applications in biomedical informatics 
260 |b Springer Nature B.V.  |c 2025 
513 |a Journal Article 
520 3 |a The diffusion model, a cutting-edge deep generative technique, is gaining traction in biomedical informatics, showcasing promising applications across various domains. This review presents an overview of the working principles, categories, and numerous applications of diffusion models in biomedical research. In medical imaging, these models, through frameworks like Denoising Diffusion Probabilistic Models (DDPMs) and Stochastic Differential Equations (SDE), offer advanced solutions for image generation, reconstruction, segmentation, and denoising. Notably, they’ve been employed in synthesizing 2D/3D medical images, MRI, and PET image reconstruction, and segmentation tasks such as labeled MRI generation. In the realm of structured Electronic Health Records (EHR) data, diffusion models excel in data synthesis, offering innovative approaches in the face of challenges like data privacy and data gaps. Furthermore, these models are proving pivotal in physiological signal domains, such as EEG and ECG, for signal generation and restoration amidst data loss and noise disruptions. Another significant application lies in the design and prediction of small molecules and protein structures. These models unveil profound insights into the vast molecular space, guiding endeavors in drug design, molecular docking, and antibody construction. Despite their potential, there are inherent limitations, emphasizing the need for further research, validation, interdisciplinary collaboration, and robust benchmarking to ensure practical reliability and efficiency. This review seeks to shed light on the profound capabilities and challenges of diffusion models in the rapidly evolving landscape of biomedical research. 
653 |a Magnetic resonance imaging 
653 |a Deep learning 
653 |a Segmentation 
653 |a Corruption 
653 |a Normal distribution 
653 |a Bioinformatics 
653 |a Medical imaging 
653 |a Diffusion models 
653 |a EKG 
653 |a Image processing 
653 |a Medical research 
653 |a Drug development 
653 |a Electronic medical records 
653 |a Electronic health records 
653 |a Probabilistic models 
653 |a Signal generation 
653 |a Image reconstruction 
653 |a Diffusion 
653 |a Molecular docking 
653 |a Image segmentation 
653 |a Data loss 
653 |a Noise reduction 
653 |a Mathematical models 
653 |a Positron emission 
653 |a Design 
653 |a Differential equations 
653 |a Informatics 
653 |a Markov analysis 
700 1 |a Yang, Liren 
700 1 |a Liu, Yan 
700 1 |a Hu, Changbao 
700 1 |a Wang, Grant 
700 1 |a Yang, Yan 
700 1 |a Tie-Lin, Yang 
700 1 |a Zhou, Xiaobo 
773 0 |t BMC Medical Informatics and Decision Making  |g vol. 25 (2025), p. 1-22 
786 0 |d ProQuest  |t Healthcare Administration Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3268430055/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3268430055/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3268430055/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch