DermaFlux: Synthetic Skin Lesion Generation with Rectified Flows for Enhanced Image Classification

TL;DR

DermaFlux is a novel rectified flow-based generative model that synthesizes realistic skin lesion images from text descriptions, significantly improving skin lesion classification performance especially with limited data.

Contribution

It introduces DermaFlux, a new text-to-image generative framework for dermatology that enhances classification accuracy by augmenting datasets with synthetic images.

Findings

Synthetic images improve classification accuracy by up to 6%.

Synthetic images outperform diffusion-based images in classification tasks.

High accuracy achieved with limited real data and synthetic augmentation.

Abstract

Despite recent advances in deep generative modeling, skin lesion classification systems remain constrained by the limited availability of large, diverse, and well-annotated clinical datasets, resulting in class imbalance between benign and malignant lesions and consequently reduced generalization performance. We introduce DermaFlux, a rectified flow-based text-to-image generative framework that synthesizes clinically grounded skin lesion images from natural language descriptions of dermatological attributes. Built upon Flux.1, DermaFlux is fine-tuned using parameter-efficient Low-Rank Adaptation (LoRA) on a large curated collection of publicly available clinical image datasets. We construct image-text pairs using synthetic textual captions generated by Llama 3.2, following established dermatological criteria including lesion asymmetry, border irregularity, and color variation. Extensive experiments demonstrate that DermaFlux generates diverse and clinically meaningful dermatology images that improve binary classification performance by up to 6% when augmenting small real-world datasets, and by up to 9% when classifiers are trained on DermaFlux-generated synthetic images rather than diffusion-based synthetic images. Our ImageNet-pretrained ViT fine-tuned with only 2,500 real images and 4,375 DermaFlux-generated samples achieves 78.04% binary classification accuracy and an AUC of 0.859, surpassing the next best dermatology model by 8%.