Recent strides in computer vision have led to promising breakthroughs in the realm of image generation. Notably, diffusion probabilistic models such as DALL-E 2, Imagen, and Stable Diffusion have demonstrated the ability to create lifelike images based on textual prompts. Yet, their potential application in the medical domain, where intricate three-dimensional image volumes are commonplace, remains largely untapped. Synthetic imagery presents a compelling avenue in the realm of privacy-preserving artificial intelligence and holds immense potential for enriching datasets with limited samples. This study seeks to assess the effectiveness of diffusion probabilistic models in synthesizing high-fidelity medical imaging data, with a particular focus on Digital Breast Tomosynthesis (DBT) images.
Denoising Diffusion Probabilistic Models for DBT data augmentation: preliminary results
Staffa M.
2024-01-01
Abstract
Recent strides in computer vision have led to promising breakthroughs in the realm of image generation. Notably, diffusion probabilistic models such as DALL-E 2, Imagen, and Stable Diffusion have demonstrated the ability to create lifelike images based on textual prompts. Yet, their potential application in the medical domain, where intricate three-dimensional image volumes are commonplace, remains largely untapped. Synthetic imagery presents a compelling avenue in the realm of privacy-preserving artificial intelligence and holds immense potential for enriching datasets with limited samples. This study seeks to assess the effectiveness of diffusion probabilistic models in synthesizing high-fidelity medical imaging data, with a particular focus on Digital Breast Tomosynthesis (DBT) images.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
