IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions

[2505.09831] IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Image and Video Processing arXiv:2505.09831 (eess) [Submitted on 14 May 2025 ( v1 ), last revised 16 Aug 2026 (this version, v3)] Title: IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions Authors: Tushar Kataria , Beatrice Knudsen , Shireen Y. Elhabian View a PDF of the paper titled IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions, by Tushar Kataria and Beatrice Knudsen and Shireen Y. Elhabian View PDF HTML (experimental) Abstract: Hematoxylin and eosin (H&E)-stained slides are central to cancer diagnosis and monitoring, visualizing tissue architecture and cellular morphology. However, H&E lacks the molecular specificity needed to distinguish cell states and functional activation. Antibody-based stains, such as immunohistochemistry (IHC), are therefore required to identify specific phenotypes (e.g., CD3$^+$ T cells or HER2-positive tumor cells) but are costly, time-consuming, and not universally available. Deep learning-based image translation methods, often termed virtual staining, offer a complementary alternative by generating virtual immunostains directly from H&E images. Most existing virtual staining methods are patch-based and operate at fixed resolutions, often requiring large datasets and additional post-hoc super-resolution models to generate high-resolution images. Furthermore, GAN- and diffusion-based approaches introduce stochasticity into generated stains which, although beneficial for visual realism in natural images, can lead to hallucinations and structural distortions that affect the accuracy and reliability required for clinical use. We propose IMPLICITSTAINER, a deterministic framework that reformulates virtual staining as a continuous pixel-level translation problem. In contrast to existing patch-based approaches, IMPLICITSTAINER formulates image translation as a continuous spatial mapping using neural implicit deep learning models. Each target-domain (IHC) pixel is predicted from a high-dimensional embedding of the corresponding source-domain H&E pixel, its local spatial neighborhood, and explicit coordinate information. IMPLICITSTAINER enables resolution-agnostic inference, improves robustness in low-data regimes, and yields deterministic, reproducible outputs. Across more than twenty baselines, IMPLICITSTAINER achieves SOTA performance on virtual staining tasks, including IHC and mIF. Subjects: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2505.09831 [eess.IV] (or arXiv:2505.09831v3 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2505.09831 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Tushar Kataria [ view email ] [v1] Wed, 14 May 2025 22:22:52 UTC (11,276 KB) [v2] Fri, 10 Apr 2026 18:52:02 UTC (21,828 KB) [v3] Sun, 16 Aug 2026 17:38:21 UTC (9,582 KB) Full-text links: Access Paper: View a PDF of the paper titled IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions, by Tushar Kataria and Beatrice Knudsen and Shireen Y. Elhabian View PDF HTML (experimental) TeX Source view license Current browse context: eess.IV < prev | next > new | recent | 2025-05 Change to browse by: cs cs.CV eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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