Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report

Understand

Despite the ever-increasing interest in applying deep learning (DL) models to medical imaging, the typical scarcity and imbalance of medical datasets can severely impact the performance of DL models.

• The generation of synthetic data that might be freely shared without compromising patient privacy is a well-known technique for addressing these difficulties.
• Inpainting algorithms are a subset of DL generative models that can alter one or more regions of an input image while matching its surrounding context and, in certain cases, non-imaging input conditions.
• Although the majority of inpainting techniques for medical imaging data use generative adversarial networks (GANs), the performance of these algorithms is frequently suboptimal due to their limited output variety, a problem that is already well-known for GANs.