Generating and evaluating synthetic data in digital pathology through diffusion models.

ABSTRACT: Synthetic data is becoming a valuable tool for computational pathologists, aiding in tasks like data augmentation and addressing data scarcity and privacy. However, its use necessitates careful planning and evaluation to prevent the creation of clinically irrelevant artifacts.This manuscript introduces a comprehensive pipeline for generating and evaluating synthetic pathology data using a diffusion model. The pipeline features a multifaceted evaluation strategy with an integrated explainability procedure, addressing two key aspects of synthetic data use in the medical domain.The evaluation of the generated data employs an ensemble-like approach. The first step includes assessing the similarity between real and synthetic data using established metrics. The second step involves evaluating th

SUBMITTER: Pozzi M 

PROVIDER: S-EPMC11574254 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature