  Foundation models have recently attracted significant attention for their
impressive generalizability across diverse downstream tasks. However, these
models are demonstrated to exhibit great limitations in representing
high-frequency components and fine-grained details. In many medical imaging
tasks, the precise representation of such information is crucial due to the
inherently intricate anatomical structures, sub-visual features, and complex
boundaries involved. Consequently, the limited representation of prevalent
foundation models can result in significant performance degradation or even
failure in these tasks. To address these challenges, we propose a novel
pretraining strategy, named Frequency-advanced Representation Autoencoder
(Frepa). Through high-frequency masking and low-frequency perturbation combined
with adversarial learning, Frepa encourages the encoder to effectively
represent and preserve high-frequency components in the image embeddings.
Additionally, we introduce an innovative histogram-equalized image masking
strategy, extending the Masked Autoencoder approach beyond ViT to other
architectures such as Swin Transformer and convolutional networks. We develop
Frepa across nine medical modalities and validate it on 32 downstream tasks for
both 2D images and 3D volume data. Without fine-tuning, Frepa can outperform
other self-supervised pretraining methods and, in some cases, even surpasses
task-specific trained models. This improvement is particularly significant for
tasks involving fine-grained details, such as achieving up to a +15% increase
in DSC for retina vessel segmentation and a +7% increase in IoU for lung nodule
detection. Further experiments quantitatively reveal that Frepa enables
superior high-frequency representations and preservation in the embeddings,
underscoring its potential for developing more generalized and universal
medical image foundation models.
