Abstract
The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. The goal of this challenge was to promote the development of deep generative models (DGMs) for medical imaging and to emphasize the need for their domain-relevant assessment via the analysis of relevant image statistics. As part of this Grand Challenge, a training dataset was developed based on 3D anthropomorphic breast phantoms from the VICTRE virtual imaging toolbox. A two-stage evaluation procedure consisting of a preliminary check for memorization and image quality (based on the Frechet Inception distance (FID)), and a second stage evaluating the reproducibility of image statistics corresponding to domain-relevant radiomic features was developed. A summary measure was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, and to identify various artifacts. 58 submissions from 12 unique users were received for this Challenge. The top-ranked submission employed a conditional latent diffusion model, whereas the joint runners-up employed a generative adversarial network, followed by another network for image superresolution. We observed that the overall ranking of the top 9 submissions according to our evaluation method (i) did not match the FID-based ranking, and (ii) differed with respect to individual feature families. Another important finding from our additional analyses was that different DGMs demonstrated similar kinds of artifacts. This Grand Challenge highlighted the need for domain-specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use.
Abstract (translated)
2023年AAPM深度生成模型大挑战的研究成果报告在本报告中。这个挑战的目标是促进深度生成模型（DGMs）在医学成像领域的发展，并通过分析相关图像统计数据强调其领域相关的评估必要性。作为这个大挑战的一部分，基于VICTRE虚拟成像工具箱中的3D人形乳腺幻灯片，开发了一个训练数据集。采用两阶段评估方案，包括初步记忆检查和图像质量评估（基于弗雷切迭代近感知距离（FID）），以及评估领域相关放射学特征的重复性。采用总结度指标对提交进行排名。此外，对提交的附加分析还检查了DGM对各个特征家族的性能，并识别出了各种 artifacts。 这个挑战共收到来自12个独特用户的58篇提交。排名前两的提交分别采用了条件图层扩散模型和生成对抗网络（GAN），接着是图像超分辨率网络。我们观察到，根据我们的评估方法，前9个提交的总体排名（i）与FID基分类排名不匹配，且（ii）在各个特征家族之间存在差异。另外一个重要的发现来自我们的附加分析是，不同的DGM展示了类似类型的伪影。这个大挑战强调了在进一步设计和部署DGM时需要进行领域特定评估，以及DGM的指定可能因其实用目的而异。它还表明，DGM的指定可能会因其在实际应用中的目的而异。
URL
https://arxiv.org/abs/2405.01822
