mlsanityData to 14 Oct 2026

CREATE-FFPE: Cross-Resolution Compensated and Multi-Frequency Enhanced FS-to-FFPE Stain Transfer for Intraoperative IHC Images

arXiv 2503.00697 ↗First posted 2 Mar 2025cs.CV cs.AI eess.IVcited by 0

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Abstract (arXiv)

In the immunohistochemical (IHC) analysis during surgery, frozen-section (FS) images are used to determine the benignity or malignancy of the tumor. However, FS image faces problems such as image contamination and poor nuclear detail, which may disturb the pathologist's diagnosis. In contrast, formalin-fixed and paraffin-embedded (FFPE) image has a higher staining quality, but it requires quite a long time to prepare and thus is not feasible during surgery. To help pathologists observe IHC images with high quality in surgery, this paper proposes a Cross-REsolution compensATed and multi-frequency Enhanced FS-to-FFPE (CREATE-FFPE) stain transfer framework, which is the first FS-to-FFPE method for the intraoperative IHC images. To solve the slide contamination and poor nuclear detail mentioned above, we propose the cross-resolution compensation module (CRCM) and the wavelet detail guidance module (WDGM). Specifically, CRCM compensates for information loss due to contamination by providing more tissue information across multiple resolutions, while WDGM produces the desirable details in a wavelet way, and the details can be used to guide the stain transfer to be more precise. Experiments show our method can beat all the competing methods on our dataset. In addition, the FID has decreased by 44.4%, and KID*100 has decreased by 71.2% by adding the proposed CRCM and WDGM in ablation studies, and the performance of a downstream microsatellite instability prediction task with public dataset can be greatly improved by performing our FS-to-FFPE stain transfer.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 2 Mar 2025.

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References · 9

  1. 2022Generative Adversarial Networkscited by 5,627
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  5. 2022HF-NeuS: Improved Surface Reconstruction Using High-Frequency Detailscited by 121
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  7. 2018How good is my GAN?cited by 74
  8. 2021Machine Learning Methods for Histopathological Image Analysis: A Reviewcited by 10
  9. 2021A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sectionscited by 3

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