Synthetic data generation method for hybrid image-tabular data using two generative adversarial networks

The generation of synthetic medical records using generative adversarial networks (GANs) has become increasingly important for addressing privacy concerns and promoting data sharing in the medical field. In this paper, we propose a novel method for generating synthetic hybrid medical records consisting of chest X-ray images (CXRs) and structured tabular data (including anthropometric data and laboratory tests) using an auto-encoding GAN ({\alpha}GAN) and a conditional tabular GAN (CTGAN). Our approach involves training a {\alpha}GAN model on a large public database (pDB) to reduce the dimensionality of CXRs. We then applied the trained encoder of the GAN model to the images in original database (oDB) to obtain the latent vectors. These latent vectors were combined with tabular data in oDB, and these joint data were used to train the CTGAN model. We successfully generated diverse synthetic records of hybrid CXR and tabular data, maintaining correspondence between them. We evaluated this synthetic database (sDB) through visual assessment, distribution of interrecord distances, and classification tasks. Our evaluation results showed that the sDB captured the features of the oDB while maintaining the correspondence between the images and tabular data. Although our approach relies on the availability of a large-scale pDB containing a substantial number of images with the same modality and imaging region as those in the oDB, this method has the potential for the public release of synthetic datasets without compromising the secondary use of data.Comment: 14 page

CT ケンサ ニ オケル ヒバク センリョウ サイテキカ ノ タメ ノ テイ センリョウ CT ガゾウ シミュレーション ギジュツ ノ カイハツ

診断可能な最低線量や最適線量を評価するためには、検討対象とした疾患や症例についてさまざまな難易度の画像を、複数の線量で撮影する必要がある。しかしながら、実際にファントム実験により線量と画像における診断能を評価するための検討を行なうには、人体と同等といえる特殊なファントムをさまざまな患者や症例に合わせて用意する必要があり、現実的ではない。また、実際にボランティアや患者に対して、症例や体格ごとにさまざまな撮影条件で複数回撮影して検討を行うことは倫理的に認められていない。このような倫理的問題がなく、現実的に実用可能なcomputed tomography(CT)検査における最適線量を決定するための有用な手法として低線量CT画像シミュレーションがある。いくつかの先行研究により報告されているシミュレーション手法の多くは、一般ユーザーにはアクセスが困難である生サイノグラムデータに依存しているため、特定のCT装置に適用が限定されている。また、生サイノグラムデータを必要としない先行研究では、シミュレーションに用いるパラメータの決定に特定のファントムを必要とするため再現が容易ではない。われわれは生サイノグラムデータや特定のファントムを使用せず、診療のために通常線量で撮影されたCT画像（以下、高線量CT画像）から低線量CT画像をシミュレーションする実用的な手法を開発した。また、本手法よりシミュレーションした低線量CT画像の画質が実際に低線量で撮影されたCT画像と同等か評価するための手法を新たに開発し、その有用性について検討した。熊本大

On the Simulation of Ultra-Sparse-View and Ultra-Low-Dose Computed Tomography with Maximum a Posteriori Reconstruction Using a Progressive Flow-Based Deep Generative Model

Ultra-sparse-view computed tomography (CT) algorithms can reduce radiation exposure for patients, but these algorithms lack an explicit cycle consistency loss minimization and an explicit log-likelihood maximization in testing. Here, we propose X2CT-FLOW for the maximum a posteriori (MAP) reconstruction of a three-dimensional (3D) chest CT image from a single or a few two-dimensional (2D) projection images using a progressive flow-based deep generative model, especially for ultra-low-dose protocols. The MAP reconstruction can simultaneously optimize the cycle consistency loss and the log-likelihood. We applied X2CT-FLOW for the reconstruction of 3D chest CT images from biplanar projection images without noise contamination (assuming a standard-dose protocol) and with strong noise contamination (assuming an ultra-low-dose protocol). We simulated an ultra-low-dose protocol. With the standard-dose protocol, our images reconstructed from 2D projected images and 3D ground-truth CT images showed good agreement in terms of structural similarity (SSIM, 0.7675 on average), peak signal-to-noise ratio (PSNR, 25.89 dB on average), mean absolute error (MAE, 0.02364 on average), and normalized root mean square error (NRMSE, 0.05731 on average). Moreover, with the ultra-low-dose protocol, our images reconstructed from 2D projected images and the 3D ground-truth CT images also showed good agreement in terms of SSIM (0.7008 on average), PSNR (23.58 dB on average), MAE (0.02991 on average), and NRMSE (0.07349 on average)

Local Differential Privacy Image Generation Using Flow-Based Deep Generative Models

Diagnostic radiologists need artificial intelligence (AI) for medical imaging, but access to medical images required for training in AI has become increasingly restrictive. To release and use medical images, we need an algorithm that can simultaneously protect privacy and preserve pathologies in medical images. To address this, we introduce DP-GLOW, a hybrid that combines the local differential privacy (LDP) algorithm with GLOW, one of the flow-based deep generative models. By applying a GLOW model, we disentangle the pixelwise correlation of images, which makes it difficult to protect privacy with straightforward LDP algorithms for images. Specifically, we map images to the latent vector of the GLOW model, where each element follows an independent normal distribution. We then apply the Laplace mechanism to this latent vector to achieve ϵ-LDP, which is one of the LDP algorithms. Moreover, we applied DP-GLOW to chest X-ray images to generate LDP images while preserving pathologies. The ϵ-LDP-processed chest X-ray images obtained with DP-GLOW indicate that we have obtained a powerful tool for releasing and using medical images for training AI