Diffusion-based lung nodule synthesis for advanced evaluation of deep learning models

The accurate detection of lung nodules in computed tomography (CT) scans is critical for the early diagnosis and treatment of lung cancer. Despite the advances of computer aided detection algorithms for nodule detection, biases in training data often compromise the generalization of deep learning models trained for this task. To address this, we present a novel methodology for synthesizing realistic lung nodules, using a fine-tuned version of the Instruct Pix2Pix framework, based on Stable Diffusion. The proposed approach enables precise control over the size and placement of the nodule within CT slices, facilitating the creation of synthetic datasets tailored for systematic bias evaluation and model validation. By generating paired CT slices with and without nodules, we provide a supervised baseline for training the diffusion model. This work demonstrates the realism of the created nodules through a clinician evaluation study, where observers often misclassified synthetic nodules as real. In fact, clinicians have mistaken synthesized nodules for real nodules in 41% of cases, a rate close to the probability level of 50%, underscoring the high fidelity of the generated images. This pioneering work shows the applicability of diffusion models for evaluating deep learning models and opens doors for future research to extend this methodology to 3D and high-quality scans. The resulting dataset and code will be made publicly available to promote further research in this domain.