Importance of Feature Extraction in the Calculation of Fréchet Distance for Medical Imaging

Fr\'echet Inception Distance is a widely used metric for evaluating synthetic image quality that utilizes an ImageNet-trained InceptionV3 network as a feature extractor. However, its application in medical imaging lacks a standard feature extractor, leading to biased and inconsistent comparisons. This study aimed to compare state-of-the-art feature extractors for computing Fr\'echet Distances (FDs) in medical imaging. A StyleGAN2 network was trained with data augmentation techniques tailored for limited data domains on datasets comprising three medical imaging modalities and four anatomical locations. Human evaluation of generative quality (via a visual Turing test) was compared to FDs calculated using ImageNet-trained InceptionV3, ResNet50, SwAV, DINO, and Swin Transformer architectures, in addition to an InceptionV3 network trained on a large medical dataset, RadImageNet. All ImageNet-based extractors were consistent with each other, but only SwAV was significantly correlated with medical expert judgment. The RadImageNet-based FD showed volatility and lacked correlation with human judgment. Caution is advised when using medical image-trained extraction networks in the FD calculation. These networks should be rigorously evaluated on the imaging modality under consideration and publicly released. ImageNet-based extractors, while imperfect, are consistent and widely understood. Training extraction networks with SwAV is a promising approach for synthetic medical image evaluation.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Medical Image Generation ACDC StyleGAN2 with DiffAugment FID 21.42 # 2
Medical Image Generation ChestX-ray14 StyleGAN2 with DiffAugment FID 3.07 # 1
Medical Image Generation SLIVER07 StyleGAN2 with DiffAugment FID 4.62 # 1

Methods