Latent Pollution Model: The Hidden Carbon Footprint in 3D Image Synthesis

Contemporary developments in generative AI are rapidly transforming the field of medical AI. These developments have been predominantly driven by the availability of large datasets and high computing power, which have facilitated a significant increase in model capacity. Despite their considerable potential, these models demand substantially high power, leading to high carbon dioxide (CO2) emissions. Given the harm such models are causing to the environment, there has been little focus on the carbon footprints of such models. This study analyzes carbon emissions from 2D and 3D latent diffusion models (LDMs) during training and data generation phases, revealing a surprising finding: the synthesis of large images contributes most significantly to these emissions. We assess different scenarios including model sizes, image dimensions, distributed training, and data generation steps. Our findings reveal substantial carbon emissions from these models, with training 2D and 3D models comparable to driving a car for 10 km and 90 km, respectively. The process of data generation is even more significant, with CO2 emissions equivalent to driving 160 km for 2D models and driving for up to 3345 km for 3D synthesis. Additionally, we found that the location of the experiment can increase carbon emissions by up to 94 times, and even the time of year can influence emissions by up to 50%. These figures are alarming, considering they represent only a single training and data generation phase for each model. Our results emphasize the urgent need for developing environmentally sustainable strategies in generative AI.

Unconditional Latent Diffusion Models Memorize Patient Imaging Data

Generative latent diffusion models hold a wide range of applications in the medical imaging domain. A noteworthy application is privacy-preserved open-data sharing by proposing synthetic data as surrogates of real patient data. Despite the promise, these models are susceptible to patient data memorization, where models generate patient data copies instead of novel synthetic samples. This undermines the whole purpose of preserving patient data and may even result in patient re-identification. Considering the importance of the problem, surprisingly it has received relatively little attention in the medical imaging community. To this end, we assess memorization in latent diffusion models for medical image synthesis. We train 2D and 3D latent diffusion models on CT, MR, and X-ray datasets for synthetic data generation. Afterwards, we examine the amount of training data memorized utilizing self-supervised models and further investigate various factors that can possibly lead to memorization by training models in different settings. We observe a surprisingly large amount of data memorization among all datasets, with up to 41.7%, 19.6%, and 32.6% of the training data memorized in CT, MRI, and X-ray datasets respectively. Further analyses reveal that increasing training data size and using data augmentation reduce memorization, while over-training enhances it. Overall, our results suggest a call for memorization-informed evaluation of synthetic data prior to open-data sharing.