ImplicitStainer: Data-Efficient Medical Image Translation for Virtual Antibody-based Tissue Staining Using Local Implicit Functions

• URL: http://arxiv.org/abs/2505.09831v1
• Date: Wed, 14 May 2025 22:22:52 GMT
• Title: ImplicitStainer: Data-Efficient Medical Image Translation for Virtual Antibody-based Tissue Staining Using Local Implicit Functions
• Authors: Tushar Kataria, Beatrice Knudsen, Shireen Y. Elhabian,
• Abstract summary: Hematoxylin and eosin (H&E) staining is a gold standard for microscopic diagnosis in pathology.<n>Hematoxylin and eosin (H&E) staining is a gold standard for microscopic diagnosis in pathology.<n>Hematoxylin and eosin (H&E) staining does not capture all the diagnostic information that may be needed.
• Score: 1.9029890402585894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hematoxylin and eosin (H&E) staining is a gold standard for microscopic diagnosis in pathology. However, H&E staining does not capture all the diagnostic information that may be needed. To obtain additional molecular information, immunohistochemical (IHC) stains highlight proteins that mark specific cell types, such as CD3 for T-cells or CK8/18 for epithelial cells. While IHC stains are vital for prognosis and treatment guidance, they are typically only available at specialized centers and time consuming to acquire, leading to treatment delays for patients. Virtual staining, enabled by deep learning-based image translation models, provides a promising alternative by computationally generating IHC stains from H&E stained images. Although many GAN and diffusion based image to image (I2I) translation methods have been used for virtual staining, these models treat image patches as independent data points, which results in increased and more diverse data requirements for effective generation. We present ImplicitStainer, a novel approach that leverages local implicit functions to improve image translation, specifically virtual staining performance, by focusing on pixel-level predictions. This method enhances robustness to variations in dataset sizes, delivering high-quality results even with limited data. We validate our approach on two datasets using a comprehensive set of metrics and benchmark it against over fifteen state-of-the-art GAN- and diffusion based models. Full Code and models trained will be released publicly via Github upon acceptance.

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