It’s All in the Details: Guiding Fine-Feature Characteristics in Artificial Medical Images using Diffusion Models
Abstract
We sought to develop a diffusion model-based framework that guides both larger anatomical structures and fine features to generate radiographic images that accurately reflect pathological characteristics. The model is based on a latent diffusion model that is extended to include coarse- and fine-feature guidance. The feedback of an independent classifier network, trained to identify malignant features, was used to provide the fine-feature guidance. We compared the accuracy of this model to that attained by one without fine-feature guidance and by a standard generative adversarial network. We used the area under ROC to compare accuracy across the networks in representing malignant features of lung nodules and gliomas on 44,924 lung CT and 6,376 MRI 2D images (annotated by trained radiologists). Statistical significance was assessed using bootstrapped p-values. For each dataset, the model generated artificial images comparable to original ones. Benign vs malignant classification accuracy without fine-feature guidance was 70% (CT), 81% (MRI). Fine-feature guidance increased the accuracy to 85.5%, 86%, for, respectively, CT and MRI images (vs unguided, p < 0.001, p < 0.001). It is feasible to use independent classifier guidance to create artificial radiographic images that accurately reflect fine features across pathologies and imaging modalities.
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